Solar photovoltaic (PV) power generation is one of the most important sources for renewable energy. However, PV power generation is entirely dependent on the amount of downward solar radiation reaching the solar cells. This is determined by uncertain and uncontrollable meteorological factors such as temperature, humidity, wind speed, and direction, as well as other factors such as topographical characteristics. Good solar radiation prediction models can increase energy output while decreasing the operation costs of PV power generation. For example, in some provinces in China, PV stations are required to upload short-term online power forecast information to power dispatching agencies. Numerous AI, statistical, and numerical weather prediction models have been used in many real-world renewable energy applications, with a focus on modeling accuracy. However, there is a need for explainable AI models that could be easily understood, analyzed, and augmented by the stakeholders. In this article, we present a compact, explainable, and lifelong learning metaheuristic-based interval type-2 fuzzy logic system for solar radiation modeling. The generated model will be composed of a small number of short IF-Then rules that have been optimized via simulated annealing to produce models with high prediction accuracy. These models are updated through a life-long learning approach to maximize their accuracy and maintain interpretability. In the process of lifelong learning, the proposed method transferred the model's knowledge to new geographical locations with minimal forgetting. The proposed method achieved good prediction accuracy and outperformed on new geographical locations other transparent and black-box models by 13.2% as well as maintaining excellent generalization ability. The resulting models have been evaluated and accepted by experts, and thanks to the generated transparency, the experts were able to augment the models with their expertise, which increased the models' accuracy.

Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this article, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that compared to multitask learning, our proposed context-aware features can achieve up to 19% more accurate results when using the residual network architecture and 3% when using ConvNeXt. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than other kinds of features.

As data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommendations at a low complexity is product quantization (PQ). PQ approaches have continued developing in recent years. This system's crucial challenge is improving PQ performance in terms of recall measures without compromising its complexity. This makes the algorithm suitable for problems that require a greater number of potentially relevant items without disregarding others, at high speed and low cost to keep up with traffic. This is the case of online shops where the recommendations for the purpose are important, although customers can be susceptible to scoping other products. A recent approach has been exploiting the notion of norm subvectors encoded in product quantizers. This research proposes a fuzzy approach to perform norm-based PQ. Type-2 fuzzy sets (T2FSs) define the codebook allowing subvectors (T2FSs) to be associated with more than one element of the codebook, and next, its norm calculus is resolved by means of integration. Our method finesses the recall measure up, making the algorithm suitable for problems that require querying at most possible potential relevant items without disregarding others. The proposed approach is tested with three public recommender benchmark datasets and compared against seven PQ approaches for maximum inner-product search. The proposed method outperforms all PQ approaches, such as norm-explicit PQ, PQ, and residual quantization up to +6%, +5%, and +8% by achieving a recall of 94%, 69%, and 59% in Netflix, Audio, and Cifar60k datasets, respectively. Moreover, computing time and complexity nearly equal those of the most computationally efficient existing PQ method in the state of the art.

In this paper, we present the Heated Stack Algorithm (HS) which is a population based multi-objective evolutionary algorithm with temperature based on type-2 fuzzy logic meta-heuristic. Temperature plays a vital role in HS being used for two distinct procedures; Sorting and Crossover. In sorting, temperature is combined with the niche distance to determine the rank order of a population front. In crossover, the temperature of two population members are compared to determine the quantity of information to take from each parent. HS is a new optimisation algorithm capable of solving constrained real-world problems. This paper will present the HS application to a real-world capacity planning problem involving networking infrastructure. To proof the algorithm applicability to wider set of problems, we will report the HS results over a subset of the constrained multi objective problems used for optimisation competitions by the IEEE Congress on Evolutionary Computation (CEEC). In these problems we have compared to the popular NSGA-II and its successor NSGA-III. By use of the hyper-volume indicator, we find that the HS outperforms NSGA-II in 84% of cases, and outperforms NSGA-III in 69% of the cases.

Existing approaches to time series prediction using fuzzy techniques do not consider time periods in a human understandable format. The current time series based fuzzy approaches lead to large feature spaces where the individual features could be prone to noise in the individual observations. This paper presents a Time Series based Interval Type 2 Fuzzy Logic System (TS-IT2FLS) where the time periods are considered in a fuzzy manner with easily understandable linguistic labels. We have performed several experiments using various data sets where the proposed TS-IT2FLS achieved 29% higher ROC-AUC score compared to other non deep-learning approaches while our suggested approach is more explainable compared to existing approaches.

In recent years, technologies such as Augmented Reality (AR) and Virtual Reality (VR) have become more popular and available to broader audiences, leading to the research and development of a myriad of extended applications. A modality for end-users to interact with these applications is through hand gestures, hence the importance of detecting the different gestures in real time. This paper presents an interval type-2 Fuzzy Rule-based System (FRBS) optimised by the Big Bang-Big Crunch (BB-BC) algorithm that uses the fingers’ position from the hand-tracking technology in extended reality (XR) headsets (namely HoloLens 2 and Oculus Quest 2) to classify the user’s hand gestures. This approach achieved an accuracy of 96.4%, and it is an interpretable model that can be understood and adjusted by end-users. The interval type-2 FRBS was tested against a type-1 FRBS and a k-nearest neighbours (KNN) model. It outperformed the type-1 FRBS and was close to the 98.9% accuracy performance of the KNN model, making our suggested approach a competitive alternative to opaque models.

Similarity measures have been widely used in applications dealing with reasoning, classification, and information retrieval. In this article, we first propose three new interval type-2 fuzzy similarity measures (IT-2 FSMs) as a dual concept of some semimetric distances between intuitionistic fuzzy sets (IFSs). We also prove that the extended IT-2 FSMs satisfy many common properties (i.e., reflexivity, transivity, symmetry, and overlapping). Experiments are carried out on a variety of datasets including UCI learning machine and real data. Comparative studies between the proposed IT-2 FSMs and the other well-known existing similarity measures (Gorzalczany, Bustince, Mitchell, Zeng, and Li as well as VSM and Jaccard) are performed. Obviously, the best results are obtained with the IT-2 FSMs being resilient to the high levels of uncertainty noise. We also prove that our IT-2 FSMs can overcome the drawbacks of some existing similarity measures based on the accuracy rate measure. In addition, the proposed IT-2 FSMs are joined with fuzzy C-means algorithm as a clustering method and the proposed system is compared against the existing clustering algorithms (type-1 fuzzy k-means, type-1, and type-2 fuzzy C-means, cluster forest, bagged clustering, evidence accumulation, and random projection). Relying on the clustering quality parameters R and C (equivalent to the standard classification accuracy), the advanced IT-2FSMs show higher classification accuracy of about 86% which outperforms nearly the other classifiers.

In computer-based search systems, similarity plays a key role in replicating the human search process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The search for images consists of establishing a correspondence between the available images and those sought by the user, by measuring the similarity between the images. In fact, image search per content is generally based on the similarity between the visual characteristics of the images. The distance function used to evaluate the similarity between images depends not only on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval system. In this article, we first constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of the characteristics extracted from the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, which is noted type-2 fuzzy nearness measure (IT2FNM). By analogy to type-2 fuzzy logic, and motivated by a near sets theory, we advanced a new fuzzy similarity measure (FSM) noted as IT2FNM. Then, we propose three new IT2FSMs and provide mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e., reflexivity, transitivity, symmetry, and overlapping). The experimental results generated using three image databases show consistent and significant results.

Strategic planning within the telecommunication domain has become necessary to ensure that networks supplying broadband are capable of being designed and updated proactively in order meet the requirements of societies which are increasingly reliant on it. However, these networks are mired in many, often contradictory, objectives. As such these networks and other similar real-world problems fall within the domain of multiobjective optimisation. Multi-objective optimisation performs well within strategic planning where no single solution is sought; instead, a set of non-dominated solutions belonging to a pareto front can be explored. The formation of the pareto front is dependent on the evaluation of solutions dominance over one another. However, traditional dominance evaluation struggles to produce a pareto front that has not become saturated with nonoptimal solutions as complexity increases, due to the number of objectives and the uncertainty that surrounds real-world problems. In this paper, we will present an interval type-2 fuzzy logic system for determining the dominance between solutions in a multi-objective problem. We have validated the performance of the proposed interval type-2 fuzzy logic system in the optimisation of a standard test problem and a real-world business problem. We have transposed the type-2 fuzzy logic system in the place of the standard dominance evaluation system of the NSGA-II algorithm and found that the proposed system outperforms the traditional and type-1 fuzzy dominance rules in both convergence and diversity of solutions towards the true pareto front.

Fuzzy Rule Based Systems (FRBSs) can suffer from incomplete and sparse rule bases as a result of selecting a small number of rules from a large universe of potential rules. This may lead to rational gaps creeping into the input output mapping, where sometimes, strongly correlated inputs displaying a linear relationship with the output do not exhibit the same behaviour during inferencing. This paper proposes a technique for identifying and rectifying such gaps for FRBSs using incomplete rule bases in real-world regression problems.

The recent advances in the field of Artificial Intelligence (AI) have led to the rapid deployment of AI systems in a variety of fields such as healthcare, financial, education etc. However, many of the AI systems are black boxes which restricts the use of these AI in applications that are highly regulated (such as financial, justice, medical, autonomous vehicles etc.) where it is necessary to provide satisfactory explanations for the decisions taken. A variety of approaches that have been proposed to tackle this problem, but these approaches generally emphasize providing satisfactory explanations for individual predictions at the cost of providing explanations at the global level. Hence, to solve these problems, in this paper, we present a hybrid deep learning type-2 fuzzy logic system which addresses these challenges by providing a highly interpretable model that can be trained using both labelled and unlabeled data. We also present a method to extract global and local explanations for this model. We also show that the presented model has reasonable performance when compared to stacked autoencoders deep neural networks.

Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.

Building Information Modelling (BIM) is a process that contain all the necessary information to manage the construction project across all its lifecycle. This benefits not only the construction industry but other industries such as utility companies that need to perform tasks inside of buildings and will need to access information about its elements. However, one of the biggest challenges is the digitalisation of the existing infrastructure. The use of semantic segmentation techniques could enable the transformation of infrastructure legacy data, such as 2D floor plans images, to open-standard BIM models. In this paper, we propose a processing pipeline to transform 2D floor plan images into BIM models. The pipeline makes use of an interval Type-2 Fuzzy Rule-based System (FRBS) that has an Intersection over Union metric value of 98.62% outperforming the Type-1 version of the model. Moreover, the proposed model is highly transparent, and it allows end-users to augment it using expert knowledge, something that is not possible in deep learning opaque-box models.

Internet of things is projected to make its way into all spheres of human life in the near future. This has been compounded with the growing demand for contactless solutions in the wake of the recent pandemic. A potential solution could involve a privacy-preserving gesture-based control system that could control a wide range of appliances. Implementing such gesture-based control systems is mainly conducted using opaque box Artificial Intelligence (AI) models. Systems based on such opaque box AI models have shown high-performance metrics on in-distribution data in a lab environment. However, they are prone to failure when exposed to real-world out-of-distribution data where they cannot be tuned or calibrated due to their complexity and opaqueness. Interval Type-2 Fuzzy Logic-based explainable AI models offer an alternative to opaque box models showing comparable performance on lab in-distribution data. In contrast, in the real world, out-of-distribution data, the type-2 fuzzy models could be easily calibrated and tuned (thanks for their explainability) to provide similar performance to those achieved on the lab in-distribution data.

The role of maintenance in modern manufacturing systems is becoming a more significant contributor to organizational benefit. World-class enterprises are pushing forward with “predict-and prevent” maintenance instead of embracing the drawbacks of reactive maintenance (or a “fail-and fix” approach). The advancement towards Artificial Intelligence (AI), Internet of Things (IoT) and cloud computing has led to a shift in maintenance paradigms with the rising interest in Machine Learning (ML) and in particular deep learning. However, opaque box AI models are complex and difficult to understand and explain to the lay user. This limits the use of these models in predictive maintenance where it is crucial to understand and analyze the model before deployment and it is imperative to understand the logic behind any given decision. This paper introduces a Type-2 Fuzzy Logic System (FLS) optimized by the Big-Bang Big-Crunch algorithm that allows maximizing the interpretability of a model as well as its prediction accuracy for the faults which may occur in future. We tested the proposed type-2 FLS model on water pumps where data was collected in real-time by our proprietary hardware deployed at Aquatronic Group Management Plc. The observations indicate that the proposed system provides a highly interpretable and accurate model for predicting the faults in equipment for building services, process and water industries. The system predictions are used to understand why a particular fault may occur, leading to improved and better-informed service visits for the customers thus reducing the disruptions faced due to equipment failures.

In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.

The field of Artificial Intelligence (AI) is enjoying unprecedented success and is dramatically transforming the landscape of the financial services industry. However, there is a strong need to develop an accountability and explainability framework for AI in financial services, based on a risk-based assessment of appropriate explainability levels and techniques by use case and domain. This paper proposes a risk management framework for the implementation of AI in banking with consideration of explainability and outlines the implementation requirements to enable AI to achieve positive outcomes for financial institutions and the customers, markets and societies they serve. The work presents the evaluation of three algorithmic approaches (Neural Networks, Logistic Regression and Type 2 Fuzzy Logic with evolutionary optimisation) for nine banking use cases. We review the emerging regulatory and industry guidance on ethical and safe adoption of AI from key markets worldwide and compare leading AI explainability techniques. We will show that the Type-2 Fuzzy Logic models deliver very good performance which is comparable to or lagging marginally behind the Neural Network models in terms of accuracy, but outperform all models for explainability, thus they are recommended as a suitable machine learning approach for use cases in financial services from an explainability perspective. This research is important for several reasons: (i) there is limited knowledge and understanding of the potential for Type-2 Fuzzy Logic as a highly adaptable, high performing, explainable AI technique; (ii) there is limited cross discipline understanding between financial services and AI expertise and this work aims to bridge that gap; (iii) regulatory thinking is evolving with limited guidance worldwide and this work aims to support that thinking; (iv) it is important that banks retain customer trust and maintain market stability as adoption of AI increases.

The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models.

Research in developmental cognitive neuroscience face challenges associated not only with their population (infants and children who might not be too willing to cooperate) but also in relation to the limited choice of neuroimaging techniques that can non-invasively record brain activity. For example, magnetic resonance imaging (MRI) studies are unsuitable for developmental cognitive studies because they require participants to stay still for a long time in a noisy environment. In this regard, functional Near-infrared spectroscopy (fNIRS) is a fast-emerging de-facto neuroimaging standard for recording brain activity of young infants. However, the absence of associated anatomical image, and a standard technical framework for fNIRS data analysis remains a significant impediment to advancement in gaining insights into the workings of developing brains. To this end, this work presents an Explainable Artificial Intelligence (XAI) system for infant's fNIRS data using a multivariate pattern analysis (MVPA) driven by a genetic algorithm (GA) type-2 Fuzzy Logic System (FLS) for classification of infant's brain activity evoked by different stimuli. This work contributes towards laying the foundation for a transparent fNIRS data analysis that holds the potential to enable researchers to map the classification result to the corresponding brain activity pattern which is of paramount significance in understanding how developing human brain functions.

In recent years, new sensor technologies have increased the accessibility of high-resolution satellite images. The information in these images can help to improve activities like urban planning and growth analysis of cities. Additionally, information extracted from these images can be used for taking decisions related to infrastructure planning, e.g. identifying objects that might interfere with network assets like underground cables. To be able to justify the cost of network planning decisions a high degree of interpretability is required. Convolutional Neural Networks (CNNs) are the state of the art for segmenting these images, but like any black box model they do not offer any explanation for their output. In this paper, we present an approach on how to use a Fuzzy Logic System (FLS) for performing explainable semantic segmentation of trees in satellite images. The FLS uses the HSV (hue, saturation, value) of the pixels as inputs and was optimized by using an evolutionary algorithm called Big Bang Big Crunch. The best configuration for the Interval Type-2 FLS has an Intersection over Union metric measure of 60.6%, which is close to the results obtained from neural network, however the proposed FLS provides interpretable outputs which is highly needed for the real-world operation especially in the telecommunication domain.

In recent years, Building Information Modelling (BIM) has become the standard for managing the lifecycle of a building. The metadata embedded to BIM models can be used onsite to enhance field worker's view with relevant information about the task. Augmented Reality (AR) is a natural candidate for enabling onsite interactions with BIM models and data, due to its ability for overlaying digital information on top of real-world objects through a digital display. However, the complexity of BIM models and the limited hardware capabilities of AR-ready devices (e.g. head mounted and mobile devices), makes it difficult to provide reliable visualizations, decreasing considerably application's performance. This paper presents a type-1 Fuzzy Logic System (T1FLS) that helps optimizing loading of BIM 3D models in an AR application. Experiments results show that the proposed T1FLS has an average frame per second (FPS) rate of 16.33 in the selected AR headset (Microsoft HoloLens). The FPS rate when using the proposed T1FLS is in average 2.33 times better than using a fixed batch size when loading BIM 3D objects.

Smart homes are a growing market in need of privacy preserving sensors paired with explainable, interpretable and reliable control systems. The recent boom in Artificial Intelligence (AI) has seen an ever-growing persistence to incorporate it in all spheres of human life including the household. This growth in AI has been met with reciprocal concern for the privacy impacts and reluctance to introduce sensors, such as cameras, into homes. This concern has led to research of sensors not traditionally found in households, mainly short range radar. There has been also increasing awareness of AI transparency and explainability. Traditional AI black box models are not trusted, despite boasting high accuracy scores, due to the inability to understand what the decisions were based on. Interval Type-2 Fuzzy Logic offers a powerful alternative, achieving close to black box levels of performance while remaining completely interpretable. This paper presents a privacy preserving short range radar sensor coupled with an Explainable AI system employing a Big Bang Big Crunch (BB-BC) Interval Type-2 Fuzzy Logic System (FLS) to classify gestures performed in an indoor environment.

Data and voice connections are an important part of any modern society, thus it is important for telecoms companies to provide resilience for network routing. Network conditions can fluctuate, redefining an optimal path through a network, which can introduce high levels of uncertainty. In this paper, we will present a type-2 fuzzy genetic system that can provide multiple routes between two locations with some ability to resist network fluctuations and uncertainty. We have tested the proposed system against a real world telecommunications data set with high levels of uncertainty, and compared this to a type-1 fuzzy system counterpart and the crisp system employed in the industry. The results indicate an average reduction in the occurrence of unique routes by 13%. Moreover, on average it increases the occurrence of a route occurring more than once by 26% when compared to a crisp version of the system. With Dijkstra's algorithm and A* being unable to perform the resilient routing task effectively.

Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, called Deep Symptoms-Based Model Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning technique. The new proposed model is validated through a set of experiments in order to demonstrate how the underlying model runs and its impact on improving the overall performance metrics. We have applied the Deep-SBM on a real historical diagnostic data provided by Cognitran Ltd. The performance of the Deep-SBM was compared against the state-of-the-art approaches and better result has been reported in terms of accuracy, precision, recall, and F-Score. Based on the obtained results, some further directions are suggested in this context. The final goal is having fault prediction data collected online relying on IoT.

Virtual Reality (VR) is a powerful tool for situate users in environments difficult to replicate in real life due to restricted access (e.g. historical sites or restricted buildings) or dangerous situations (e.g. working on top of a pole) among other examples. Such simulations can be used for allowing users to experience real-life situations in a controlled environment, enabling the study of interesting phenomena, such as user's engagement. This paper introduces a work-in-progress mobile VR app that recreates everyday working scenarios, using a mixture of 3D modelled environments and media streamed resources (e.g. 360 videos) to attract talent for specific roles in a company, by providing a better understanding of the activities involved in those roles. We complement this with preliminary results of a user evaluation, and the results of an engagement evaluation provided via a fuzzy logic system (FLS).

Recent advancements in technology have led to a great interest in the use of Unmanned Aerial Vehicles (UAVs) for a vast array of applications such as real time site monitoring, target search and destroy and UAVs being used as mobile sinks to collect data from Internet of Things (IoT) devices. This is mainly due to their autonomy, high mobility, ease of deployment and affordable nature. A group of UAVs can be used collectively to bring a coordinated effort in task execution, allowing more tasks to be completed in a wider area and in the shortest possible time. However, using multiple UAVs presents some challenges for efficient cooperation. UAVs are resource constrained due to being battery powered and this limits the permissible flight time. Therefore, it is necessary to intelligently manage their operation given the limited resources and other constraints associated with the mission. In this paper, we propose a multi-objective UAV task assignment model to support spatio-temporally distributed events raised by static IoT devices, using a discrete Non- Dominated Sorting Genetic Algorithm II (NSGA-II). This model assigns the most suitable UAV(s) to serve at the different event locations ensuring that none of the constraints are violated. The performance of the algorithm was evaluated through numerical simulations and compared to a similar implementation using Mixed Integer Linear Programming (MILP). Results show an improvement of 7.9% in the total energy consumption for all UAVs while ensuring that all the temporal constraints are not violated.

This paper provides a novel and better understanding of the performance potential of a nonsingleton (NS) fuzzy system over a singleton (S) fuzzy system. It is done by extending sculpting the state space works from S to NS fuzzification and demonstrating uncertainties about measurements, modeled by NS fuzzification: first, fire more rules more often, manifested by a reduction (increase) in the sizes of first-order rule partitions for those partitions associated with the firing of a smaller (larger) number of rules-the coarse sculpting of the state space; second, this may lead to an increase or decrease in the number of type-1 (T1) and interval type-2 (IT2) first-order rule partitions, which now contain rule pairs that can never occur for S fuzzification-a new rule crossover phenomenon-discovered using partition theory; and third, it may lead to a decrease, the same number, or an increase in the number of second-order rule partitions, all of which are system dependent-the fine sculpting of the state space. The authors' conjecture is that it is the additional control of the coarse sculpting of the state space, accomplished by prefiltering and the max-min (or max-product) composition, which provides an NS T1 or IT2 fuzzy system with the potential to outperform an S T1 or IT2 system when measurements are uncertain.

The recent years have witnessed a growing anticipation for the positive transformation of industries which adopt Artificial Intelligence (AI) for the core areas of their business activities. However, the effectiveness and reliability of such AI systems must comprise the ability to explain their data acquisition, the underlying algorithms operations and the final decisions to stakeholders, including regulators, risk managers, supervisors and end-users among others. There are plenty of areas where Explainable AI (XAI) holds the promise to be a major disruptor. Particularly, in Telecommunication Service Providers (TSPs) which is a core business activity relating to the workforce allocation domain, which, involves costly and time-consuming scheduling processes. This paper focuses on the construction of an XAI framework to assist workforce allocation based on a big bang- big crunch interval type-2 fuzzy logic system (BB-BC IT2FLS) for modelling and scaling goal-driven simulation (GDS) problems, specifically within the telecommunications industry. The obtained results reported the proposed XAI system produces similar results to opaque box models like Neural Networks (NNs) and LSTM Recurrent NNs while being able to explain the decision and operation of the employed system.

Network connectivity has become an essential part of our modern society, so it is important for telecoms organizations to be able to provide resilience to network faults. A solution to this is to provide multiple distinct routes between network locations. Furthermore, network conditions can fluctuate which in turn introduces high uncertainty levels. Due to the nature of this problem traditional greedy routing methods such as Dijkstra's algorithm and A* struggle to provide an optimal solution, as they will use the best route through a network for the first route and leave a more expensive route for a secondary route.In this paper, we will present a fuzzy genetic system that can provide multiple routes between two locations that is robust to uncertain environments. The proposed system is based on the combination of Dijkstra's Algorithm, Genetic Algorithm and Type 1 Fuzzy Logic. We have tested the proposed system with real-world telecoms infrastructure data, which is affected by high levels of uncertainty. The results showed that the proposed system can generate more consistent routes in 30% more cases and finding consistent routes in 17.5% more cases when compared to Dijkstra's Algorithm. The fuzzy implementation of the proposed system increased the number of consistent routes by a further 14.90%.

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low-dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the hierarchical interval type-2 beta fuzzy system (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of the HT2BFS in a multi-objective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the multi-objective immune programming algorithm. The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the hybrid harmony search algorithm. In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state-of-the-art approaches on noise-free and noisy time series prediction datasets and regression problems. The results clearly demonstrate a great improvement in accuracy rate, convergence speed, and the number of used rules as compared to other existing approaches.

The use of hesitant fuzzy linguistic term sets (HFLTS) contributes to the elicitation of comparative linguistic expressions (CLEs) in decision contexts when experts hesitate among different linguistic terms to provide their assessments. Since the existing representation models for linguistic expressions based on HFLTS do not properly consider the uncertainty caused by the inherent vagueness of such linguistic expressions, it is necessary to improve their modeling to cope with such vagueness. In this paper, we propose a new fuzzy envelope for the HFLTS in form of type-2 fuzzy sets for representing CLEs. Such an envelope overcomes the limitation of existing representations in coping with inherent uncertainties and facilitates the processes of computing with words for linguistic decision making problems dealing with CLEs.

Recent years have witnessed a widespread in the use of interval type-2 fuzzy logic systems (IT2 FLSs) in real-world applications. It has been shown recently that interval type-2 fuzzy sets (IT2 FSs) are more general than interval-valued fuzzy sets (IV FSs) [1]. Hence, there is a need to explore the capabilities of the more general forms of IT2 FSs (beyond IV FSs) and the applications areas they will be more suitable for. In addition, there is a need to develop the theory of the general forms of IT2 FLSs (gfIT2 FLSs), which employ IT2 FSs that are not equivalent to IV FSs and can have nonconvex secondary membership functions (MFs). Although these systems could be considered within the scope of general type-2 FLSs (GT2 FLSs), the practical implementation of GT2 FLSs has traditionally required the secondary MFs to be convex and normal type-1 fuzzy sets (T1 FSs). In addition, the type-reduction operation still presents a challenge for GT2 FLSs because of its computational complexity. In this paper, we present a complete framework for a type-2 FLS that uses the most recent perception of IT2 FSs (the so called general forms of interval type-2 fuzzy sets, gfIT2 FSs), whose secondary grades can be nonconvex T1 FSs. This framework includes new equations for the meet and join operations of gfIT2 FSs, as well as a new type reduction procedure for the type-2 FLS involving gfIT2 FSs. In addition, we present the type-2 FLS operation for singleton and nonsingleton fuzzification. We will introduce the various operations employed within a gfIT2 FLSs, from fuzzification (including singleton and nonsingleton) to inference, type-reduction, and defuzzification. We will also present two examples in which these gfIT2 FSs arise naturally when modeling sonar sensors input noise and the antecedents/consequents from a survey including different users.

Employing effective optimization strategies in organizations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organizations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilized is a challenging problem as there are many factors that can impact the overall performance of the organization. We have developed a system that optimizes to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimization problems, which means there are many objectives to consider when optimizing, and there is much uncertainty in the environment. The latest version of the system uses a multiobjective genetic algorithm as its core optimization logic, with modifications such as fuzzy dominance rules (FDRs), to help overcome the issues associated with many-objective optimization. The system also utilizes genetically optimized type-2 fuzzy logic systems to better handle the uncertainty in the data and modeling. This paper shows the genetically optimized type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1 million a year, the reduction of over 2500 t of CO2and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.

Efficient utilization of resources (engineers) is critical to optimal service delivery in service-based industries, such as water, electricity or telecom companies. One of the ways in which efficiency can be improved is by optimizing the geographic area in which the engineers operate. This process is known as Work Area (WA) optimization and it is a sub-domain of workforce optimization. In previous attempts to tackle the work area optimization problem, various machine learning algorithms have been proposed to optimize the work areas. However, they don't provide much insight into when and which WAs must be optimized. This is an important question, as optimizing a WA which is already optimum can lead to a loss in efficiency as the engineers can take time to adjust to the new WA. This paper presents a Type-2 Fuzzy Logic System (FLS) which has been optimized by the Big-Bang Big-Crunch approach to allow maximizing the model interpretability and allow good prediction for the future performance of WAs. We can then use these predictions to determine which WAs must be optimized. We compare the proposed type-2 FLS model against a type-1 counterpart, a stacked autoencoder deep neural network and single hidden layer neural network. The results show that the proposed type-2 FLS provides a highly interpretable model which predicts the future performance of WAs with a reasonable error rate. In addition, it also provides the necessary insight into which parameters of the WA determine the future performance. This allows answering the question of when and which WAs must be optimized.

In Utility based industries that employ a large mobile workforce, efficient utilization of field engineers is key to optimal service delivery. The utilization of the engineers can be improved by predicting the future performance of work areas by using machine learning tools such as Deep Neural Networks (DNNs).The dramatic success of DNNs has led to an explosion of its applications. However, the effectiveness of DNNs can be limited by the inability to explain how the models arrived at their predictions.In this paper, we present a novel Type-2 Fuzzy Logic System (FLS) whose inputs are preprocessed by a Stacked Autoencoder Neural Network to add some interpretability to a Deep Neural Network model. The proposed type-2 FLS will contain a small rule set with a small number of antecedents per rule to maximize the model's interpretability. We also present an algorithm which can be used to efficiently train the proposed model.We will compare the proposed model with a Standard Stacked Autoencoder Deep Neural Network, a Multi-Layer Perceptron (MLP) neural network and an Interval Type-2 Fuzzy Logic System.The results show that even though the Standard Stacked Autoencoder and MLP Neural Networks have better performance, they do not provide any insight into the reasoning behind the predictions. The Proposed model, on the other hand, provides better result than the standalone type-2 FLS and a comparable performance to the neural networks and provides a little bit of insight into the decision-making process. Without this insight, we cannot be sure why there is a drop in the performance and we need to further analyze the WA before we can take any decision. This leads to quicker decision making and potentially improving the efficiency of the engineers.

Planning of infrastructure's provision and maintenance tasks is commonly done in a planning office using paper maps and desktop applications. However, any infrastructure plan has to be verified on location before being submitted to the responsible authorities. This task is usually accomplished by taking paper maps to the field and annotating them on site, or in the best case, using two-dimensional (2D) maps on mobile devices. Augmented reality (AR) can provide enhanced experiences of real-world situations by overlaying key information and three-dimensional (3D) visualizations when needed, thus supporting decision-making processes. AR could support land surveyors and mobile planners with a graphical overlay of the planned changes, highlighting relevant information and assets in their field of view. This paper presents an AR application, which uses interval type-2 fuzzy logic mechanisms to visualise immersive 3D georeferenced data; supporting planning and designing of infrastructure by directly modifying data to incorporate required changes, without the need of any post-processing. Immersive visual feedback is provided via a head mounted display (HMD), enhancing user's 3D spatial perception of georeferenced data.

This paper presents a self-adaptive autonomous online learning through a general type-2 fuzzy system (GT2 FS) for the motor imagery (MI) decoding of a brain-machine interface (BMI) and navigation of a bipedal humanoid robot in a real experiment, using electroencephalography (EEG) brain recordings only. GT2 FSs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) the maximum number of EEG channels is limited and fixed; 2) no possibility of performing repeated user training sessions; and 3) desirable use of unsupervised and low-complexity feature extraction methods. The novel online learning method presented in this paper consists of a self-adaptive GT2 FS that can autonomously self-adapt both its parameters and structure via creation, fusion, and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structure identification is based on an online GT2 Gath-Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models), which are learnt in a continous (trial-by-trial) non-iterative basis. The effectiveness of the proposed method is demonstrated in a detailed BMI experiment, in which 15 untrained users were able to accurately interface with a humanoid robot, in a single session, using signals from six EEG electrodes only.

Supplier companies aim to pursue an efficient resource allocation to different jobs over specific times and other constraints. Dynamic and unstructured environments and real-world situations incorporate a large amount of uncertainties which are difficult to model. This paper proposes a type-2 Fuzzy Logic System (FLS) for estimating the extra number of engineers required to allocate a certain number of jobs. The type-2 FLS was trained from the knowledge extracted dynamically from input data in order to estimate corresponding outputs for unseen data. The proposed methodology has been applied to real-world service provider industry in the workforce allocation domain. The system generated sensible results which outperformed the type-1 fuzzy logic based counterpart over unseen data.

In recent years, Augmented Reality (AR) started transitioning from an experimental technology to a more mature area, with new types of applications in entertainment, marketing, education, retail, transportation, manufacturing, construction, and other industries. One of the main challenges for AR-based field service tools is to help users to correctly locate company's assets and infrastructure in the field. This paper presents an AR system using private maps to find company's assets to support field workforce tasks. The AR system is based on fuzzy logic mechanisms to provide the user with directions for asset location by comparing his/her current position with assets' location in real-time. Auditory and visual feedback is provided via a head mounted display (HMD), enhancing user's perception to achieve human augmentation.

This paper presents a type-2 Fuzzy Logic System (FLS) to support technical employees in finding company's assets in outdoor settings. The system provides the user with directions for asset location by comparing his/her current position with assets' location in real-time, giving auditory and visual feedback via a Head Mounted Display (HMD). We carried out 35 path explorations in a predefined area to test the system. The results indicated that the proposed type-2 fuzzy logic produces better performance than the type-1 based fuzzy system, giving more precise indications to reach asset's position.

Recently, the theory regarding interval type-2 fuzzy sets and fuzzy logic systems has been further developed. In the first instance, the concepts of interval type-2 fuzzy sets were broadened, proving that this class of sets include some which are different from interval-valued sets. In a later work, the set theoretic operations of union and intersection on these sets were studied and presented under the framework of general type-2 fuzzy sets. This stimulated and motivated the further study of fuzzy logic systems using these new sets, which have been referred to as the general forms of interval type-2 fuzzy sets. However, as usual when a new fuzzy logic framework is presented, only the singleton version of these systems was introduced. In this work we aim to generalise that work, introducing the non-singleton general forms of interval type-2 fuzzy logic systems. We will present a real example on how to use these non-singleton fuzzy logic systems in real world applications.

Sequences classification problems in recorded videos are often very complex and have too much uncertainty. In many application domains, such as video event activity detection, sequences of events occurring over time need to be studied in order to summarize the key events from the video clips. In most existing adaptive sequences classification systems, Dynamic Time Warping (DTW) and Gaussian Mixture Mode (GMM) are used as the core techniques in measuring similarity between two temporal sequences, which may vary in speed. Hence, there is a need to develop video event detection systems capable of classifying important events within long video sequences. This paper presents a novel system based on DTW and Interval Type-2 Fuzzy Logic Systems employing the Big Bang Big Crunch (BB-BC) algorithm for video activity detection and classification of critical events from the large-scale data of soccer videos.

In real world optimization problems there are often multiple objectives to consider. However, with traditional multi-objective optimization algorithms, like the Non-Dominated Sorting Genetic Algorithm, NSGA-II, one solution is not produced at the end of the process but a set of non-dominated solutions. This set of solutions make up what is known as the Pareto front. The Pareto front relies on calculating the dominance of each solution the multi-objective algorithm produces. Traditional dominance calculations are reasonable for a small number of objectives. However, the more objectives there are in the problem, the more unsuitable these dominance calculations become. This leads to poor selection criteria and ultimately a weaker form of optimization when compared to a small number of objectives. In this paper, we present a fuzzy logic system for computing dominance between two solutions. We have evaluated this fuzzy logic system in optimizing a set of black box test problems. In addition, we have also applied it to a real world many-objective system that optimizes five conflicting objectives, in the telecommunications domain. The implementation of the fuzzy logic system has led to the NSGA-II algorithm with Fuzzy Dominance Rules (FDRs) being able to perform better in a number of black box tests and improving the results of our real-world many-objective optimization problem, with a statistically significant improvement to the hypervolume of 5.46%.

Classification techniques are becoming essential in the financial world for reducing risks and possible disasters. Managers are interested in not only high accuracy, but in interpretability and transparency as well. It is widely accepted now that the comprehension of how inputs and outputs are related to each other is crucial for taking operative and strategic decisions. Furthermore, inputs are often affected by contextual factors and characterized by a high level of uncertainty. In addition, financial data are usually highly skewed toward the majority class. With the aim of achieving high accuracies, preserving the interpretability, and managing uncertain and unbalanced data, this paper presents a novel method to deal with financial data classification by adopting type-2 fuzzy rule-based classifiers (FRBCs) generated from data by a multiobjective evolutionary algorithm (MOEA). The classifiers employ an approach, denoted as scaled dominance, for defining rule weights in such a way to help minority classes to be correctly classified. In particular, we have extended PAES-RCS, an MOEA-based approach to learn concurrently the rule and data bases of FRBCs, for managing both interval type-2 fuzzy sets and unbalanced datasets. To the best of our knowledge, this is the first work that generates type-2 FRBCs by concurrently maximizing accuracy and minimizing the number of rules and the rule length with the objective of producing interpretable models of real-world skewed and incomplete financial datasets. The rule bases are generated by exploiting a rule and condition selection (RCS) approach, which selects a reduced number of rules from a heuristically generated rule base and a reduced number of conditions for each selected rule during the evolutionary process. The weight associated with each rule is scaled by the scaled dominance approach on the fuzzy frequency of the output class, in order to give a higher weight to the minority class. As regards the data base learning, the membership function parameters of the interval type-2 fuzzy sets used in the rules are learned concurrently to the application of RCS. Unbalanced datasets are managed by using, in addition to complexity, selectivity and specificity as objectives of the MOEA rather than only the classification rate. We tested our approach, named IT2-PAES-RCS, on 11 financial datasets and compared our results with the ones obtained by the original PAES-RCS with three objectives and with and without scaled dominance, the FRBCs, fuzzy association rule-based classification model for high-dimensional dataset (FARC-HD) and fuzzy unordered rules induction algorithm (FURIA), the classical C4.5 decision tree algorithm, and its cost-sensitive version. Using nonparametric statistical tests, we will show that IT2-PAES-RCS generates FRBCs with, on average, accuracy statistically comparable with and complexity lower than the ones generated by the two versions of the original PAES-RCS. Further, the FRBCs generated by FARC-HD and FURIA and the decision trees computed by C4.5 and its cost-sensitive version, despite the highest complexity, result to be less accurate than the FRBCs generated by IT2-PAES-RCS. Finally, we will highlight how these FRBCs are easily interpretable by showing and discussing one of them.

The final goal of this study is to adapt the concept of fuzzy entropy of De Luca and Termini to deal with type-2 fuzzy sets. We denote this concept type-2 fuzzy entropy set. However, the construction of the notion of entropy measure on an infinite set, such us [0,1], is not effortless. For this reason, we first introduce the concept of quasi-entropy of a fuzzy set on the universe [0,1]. Furthermore, whenever the membership function of the considered fuzzy set in the universe [0,1] is continuous, we prove that the quasi-entropy of that set is a fuzzy entropy in the sense of De Luca and Termini. Finally, we present an illustrative example, where we use type-2 fuzzy entropy sets instead of fuzzy entropies in a classical fuzzy algorithm.

In real world applications it can often be difficult to determine which optimization algorithm to use. This is especially true if the problem has multiple objectives, which is a common occurrence in real world applications. Both Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithms have been explored, often being compared to each other. As problems are scaled up to more objectives, the suitability of these algorithms can change and would need to be modified. The most common multi-objective algorithms in use are Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO), which we are choosing to evaluate, as they can be tested in both their single and multi-objective forms. Real world applications often come with many conditions and constraints. The one being examined in this paper is concerned with the optimal design of working areas, for a large scale mobile workforce in the telecommunications utilities domain. This paper presents the suitable underlying algorithm to use for this problem with the aim of maximizing the utilization of the workforce, whilst having balanced and manageable working areas. The results show that genetic algorithms, in both its single and multi-objective forms, may be the most suitable option for this problem, when compared to PSO and MOPSO algorithms. The results also show that organizing the problem geographically helps the particle swarm algorithms.

The recent years have witnessed an increase in the use of augmented and virtual reality systems, changing the way we interact with our environments. Such systems are commonly associated with advertising, entertainment, medicine, training and education. However, with the increasing acceptance and availability of mobile and wearable devices (e.g. head-mounted displays (HMD)), the use of these technologies is moving towards professional and industrial environments, where they would be able to support employees in their daily tasks, increasing customer satisfaction and reducing business costs. This paper presents an innovative Mixed Reality (MR) system to assist field workforce in remote locations. As part of the overall implementation, the MR system uses fuzzy logic mechanisms to improve accuracy in user tracking and object monitoring, allowing the correct representation of users and objects in the Graphical User Interfaces (GUIs), and improving the experience for users.

Recently, it has been shown that interval type-2 fuzzy sets (IT2FSs) are more general than interval-valued fuzzy sets (IVFSs), and some of these IT2FSs can actually be non-convex. Although these IT2FSs could be considered within the general type-2 fuzzy sets' (GT2FSs) scope, this latter have always been studied and developed under certain conditions considering the convexity and normality of their secondary grades. In recent works the operations of intersection and union for GT2FSs have been extended to include non-convex and non-normal secondary grades. Hence, there is a need to develop the theory for those general forms of interval type-2 fuzzy logic systems (gfIT2FLSs) which use IT2FSs that are not equivalent to IVFSs and can have non-convex secondary grades. Furthermore, we will present the mathematical tools to define the inference engine. This work aims to introduce the basic structure of such gfIT2FLSs, paying special attention to those blocks presenting significant differences with the already well known type-2 FLSs which employ IT2FSs which are equivalent to IVFSs (we will term IVFLSs).

The recent years have witnessed significant progress in the automation of sports video summarization. The vast majority of the techniques applied to sports video classifications involved black box techniques such as support vector machines (SVMs) and neural networks, which do not provide models that could be easily analysed and understood by human users. Video scenes can be regarded as continuous sequences of images, but the classification problem is much more complicated than single image classification due to the dynamic nature of the video sequence and the associated changes in light conditions, background, camera angle, occlusions, indistinguishable scene features, etc. In order to handle such high levels of uncertainties in video scenes classification, we introduce a system based on Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) whose parameters are optimized by the Big Bang-Big Crunch (BB-BC) algorithm which allows for real time scenes classification using optimized rules in broadcasted soccer matches video. The proposed system allows achieving relatively high classification accuracy with a small number of rules, thus increasing the system interpretability.

In real world optimization problems there are often multiple objectives to consider. However with regular multiobjective genetic algorithms the more objectives there are the more of a problem this becomes for the Pareto front. This is why solutions for Many Objective Problems should be explored. Many objective problems differ from multi-objective problems in that they have more than three objectives [1], [2], [3]. The problem faced by many objective systems is that the more objectives there are the more likely that more solutions will appear on the Pareto front, especially if the objectives are conflicting. This is a problem in two instances, the first is that the genetic algorithm finds it difficult to distinguish between solutions for parent selection, the second is that the output of the system will usually give a big portion of the entire population set. This means that it might be very difficult for users to choose a single solution to apply to the given real-world problem. This paper presents a novel many objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization. This system was employed in real world scheduling problems to handle the high uncertainty levels associated with these domains. We will present a distance measure to avoid the problems associated with the selection of one solution from the Pareto front of Many Objective Problems. The system in this paper uses five objectives from a real world many-objective problem where the objectives are conflicting and as a result the Pareto front becomes saturated with solutions. The distance metric will help to evaluate if optimizing fuzzy systems using a genetic algorithm improves the performance of the system, comparing both optimized and un-optimized type-1 and type-2 fuzzy sets. The results show that optimizing the membership functions of fuzzy sets using a genetic algorithm improved the overall performance of the fuzzy systems and that the distance metric helps to distinguish between the better solutions on the Pareto front. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities companies.

Sports video summarization and classification is becoming a very important topic due to the pressing need to automatically classify sports scenes to enable better sport analysis, refereeing, training and advertisement. The vast majority of the techniques applied to sports video classifications involved black box techniques such as support vector machines (SVMs) and neural networks, which do not provide models that could be easily analysed and understood by human users. Fuzzy logic classification systems provide white box techniques encompassing linguistic rules and labels that can easily be understood and analysed by humans. However, the traditional fuzzy classification systems can result in huge rule bases due to the curse of the dimensionality problem. This paper presents a fuzzy logic based system for sports video scene classification using the Big Bang-Big Crunch technique to optimize the rules, thus producing high classification accuracy with a small number of rules, increasing system interpretability. Various experiments conducted on football videos demonstrated that the system produces a fuzzy classification system with only eight rules with average classification accuracy of 83%, outperforming other black box classification models which employ neural networks.

Automatic recognition of behaviours and events from visual data is an emerging topic in video surveillance. These methods promise the ability to derive contextual awareness for a scene and may further enable the ability to predict the intentions of the subject. This paper describes a novel system for analysing human behaviours in the context of a video surveillance application. This may be used to distinguish between normal and anomalous behaviours. We propose a novel framework for the application of behaviour recognition and summarisation using interval type-2 fuzzy logic classification systems (IT2FLS). We employ the evolutionary-based technique Big Bang Big Crunch (BB-BC) to automatically optimise parameters of membership functions (MFs) and rules in the IT2FLSs. Our analysis shows that the BB-BC IT2FLS is able to robustly recognise behaviours and furthermore outperforms both its' conventional IT2FLS (which does not employ fuzzy classification techniques) and Type-1 FLSs (T1FLSs) counterparts in addition to non-fuzzy recognition methods.

In industries which employ large numbers of mobile field engineers (resources), there is a need to optimize the task allocation process. This particularly applies to utility companies such as electricity, gas and water suppliers as well as telecommunications. The process of allocating tasks to engineers involves finding the optimum area for each engineer to operate within where the locations available to the engineers depends on the work area she/he is assigned to. This particular process is termed as work area optimization and it is a sub-domain of workforce optimization. The optimization of resource scheduling, specifically the work area in this instance, in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. In previous attempts to tackle workforce optimization in real world scenarios, single objective optimization algorithms employing crisp logic were employed. The problem is that there are usually many objectives that need to be satisfied and hence multi-objective based optimization methods will be more suitable. Type-2 fuzzy logic systems could also be employed as they are able to handle the high level of uncertainties associated with the dynamic and changing real world workforce optimization and scheduling problems. This paper presents a novel multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization, which was employed in real world scheduling problems. This system had to overcome challenges, like how working areas were constructed, how teams were generated for each new area and how to realistically evaluate the newly suggested working areas. These problems were overcome by a novel neighborhood based clustering algorithm , sorting team members by skill, location and effect, and by creating an evaluation simulation that could accurately assess working areas by simulating one day's worth of work, for each engineer in the working area, while taking into account uncertainties. The results show strong improvements when the proposed system was applied to the work area optimization problem , compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities and telecommunications companies.

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social science evaluation confirming the strong user acceptance of the system.

In this paper, we review the definition and basic properties of the different types of fuzzy sets that have appeared up to now in the literature. We also analyze the relationships between them and enumerate some of the applications in which they have been used.

This letter makes some observations about “Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: Towards a wide view on their relationship,”IEEE Trans. Fuzzy Systemsthat further support the distinction between an interval type-2 fuzzy set (IT2 FS) and an interval-valued fuzzy set (IV FS), points out that all operations, methods, and systems that have been developed and published about IT2 FSs are, so far, only valid in the special case when IT2 FS = IVFS, and suggests some research opportunities.

In this paper we will present two theorems for the join and meet operations for general type-2 fuzzy sets with arbitrary secondary memberships, which can be non-convex and/or non-normal type-1 fuzzy sets. These results will be used to derive the join and meet operations of the more general descriptions of interval type-2 fuzzy sets presented in [1], where the secondary grades can be non-convex. Hence, this work will help to explore the potential of type-2 fuzzy logic systems which use the general forms of interval type-2 fuzzy sets which are not equivalent to interval valued fuzzy sets. Several examples for both general type-2 and the more general forms of interval type-2 fuzzy sets are presented.

The area of ambient-assisted living (AAL) focuses on developing new technologies, which can improve the quality of life and care provided to elderly and disabled people. In this paper, we propose a novel system based on 3-D RGB-D vision sensors and interval type-2 fuzzy-logic-based systems (IT2FLSs) employing the big bang-big crunch algorithm for the real-time automatic detection and summarization of important events and human behaviors from the large-scale data. We will present several real-world experiments, which were conducted for AAL-related behaviors with various users. It will be shown that the proposed BB-BC IT2FLSs outperform the type-1 fuzzy logic system counterparts as well as other conventional nonfuzzy methods, and the performance improves when the number of subjects increases.

E-learning is becoming increasingly more popular. However, for such platforms (where the students and tutors are geographically separated), it is necessary to estimate the degree of students' engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in large-scale online learning platforms. When the number of attendees is large, it is essential to obtain overall engagement feedback, but it is also challenging to do so because of the high levels of uncertainty associated with the environments and students. To handle such uncertainties, we present a type-2 fuzzy logic based system using visual RGB-D features including head pose direction and facial expressions captured from a low-cost but robust 3D camera (Kinect v2) to estimate the engagement degree of the students for both remote and on-site education. This system enriches another self- learning type-2 fuzzy logic system which provides the instructors with suggestions to vary their teaching means to suit the level of course students and improve the course instruction and delivery. This proposed dynamic e-learning environment involves on-site students, distance students, and a teacher who delivers the lecture to all attending onsite and remote students. The rules are learned from the students' behavior and the system is continuously updated to give the teacher the ability to adapt the lecture delivery instructional approach to varied learners' engagement levels. The efficiency of the proposed system has been evaluated through various real-world experiments in the University of Essex iClassroom on a sample of thirty students and six teachers. These experiments demonstrate the efficiency of the proposed interval type-2 fuzzy logic based system to handle the faced uncertainties and produce superior improved average learners' engagements when compared to type-1 fuzzy systems and nonadaptive systems.

In 1996, Zadeh coined Computing With Words (CWWs) to be a methodology in which words are used instead of numbers for computing and reasoning. One of the main challenges which faced the CWWs paradigm has been modelling words adequately. Mendel has pointed out that the CWWs paradigm should employ type-2 fuzzy logic to model words. This paper proposes employing an Enhanced Interval Approach (EIA) to create Linear General Type-2 (LGT2) fuzzy sets from Interval Type-2 (IT2) fuzzy sets to encode words for CWWs applications. We have performed experiments on 18 words belonging to 3 different linguistic variables (having 6 linguistic terms each). Interval data has been collected from 17 subjects and 18 linguistic terms have been modeled with IT2 fuzzy sets using EIA. The proposed conversion approach uses several key points within the parameters of IT2 fuzzy sets to redesign the linguistic variable using LGT2 fuzzy sets. Both IT2 and LGT2 fuzzy sets have been evaluated within a CWWs Framework, which aims to mimic the ability of humans to communicate and manipulate perceptions via words. The comparison results show that LGT2 fuzzy sets can be better than IT2 fuzzy sets in mimicking human reasoning as well as learning and adaptation since the progressive Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for LGT2 based CWWs Framework converge faster and are lower than those for IT2 based CWWs Framework.

In this paper, we will present design methods for Single input IT2-FLCs (SIT2-FLCs) and we will introduce an online tuning mechanism to enhance their control system performance. The most important feature of the SIT2-FLC is the closed form output presentation which is defined in a two dimensional domain. Based on this structural information, we will present design methods for SIT2-FLCs composed of 3 rules to produce a Smooth SIT2-FLC (S-SIT2-FLC) and an Aggressive SIT2-FLC (A-SIT2-FLC) by only tuning a single parameter. It will be shown that the S-SIT2-FLC will result in a potentially more robust control performance in comparison A-SIT2-FLC. However, the transient state and disturbance rejection performance of the S-SIT2-FLC might degrade in comparison to the A-SIT2-FLC. This drawback will be solved by tuning the FOU size of the SIT2-FLCs to provide a trade-off between the robust control performance of the S-SIT2-FLC and the acceptable transient and disturbance rejection performance of the A-SIT2-FLC structure. Thus, we will present a Gradient- Descent (GD) based online tuning mechanism to enhance both the transient state and disturbance rejection performances of the SIT2-FLCs while preserving a certain degree of the robustness against nonlinearities and disturbances. We will present simulation results where the GD based SIT2-FLC (GD-SIT2- FLC) is compared with the S-SIT2-FLC and the A-SIT2-FLC structures. Moreover, we will compare the performance GD-SIT2-FLC with a robust self-tuning Type-1 (T1) FLC which has a fuzzy based tuning mechanism. The results will show that the GD-SIT2-FLC enhances both the transient state and disturbance rejection performances when compared to the IT2 and robust self-tuning T1 counterparts.

In utility based service industries with a large mobile workforce, there is a need to optimize the process of allocating engineers to tasks (i.e. fixing faults, installing new services, such as internet connections, gas or electricity etc.). Part of the process of optimizing the resource allocation to tasks involves finding the optimum area for an engineer to operate within, which we term as work area optimization. Work area optimization in large businesses can have a noticeable impact on business costs, revenues and customer satisfaction. However when attempting to optimize the workforce in real world scenarios, mostly single objective optimization algorithms are used while employing crisp logic. Nevertheless, there are many objectives that need to be satisfied and hence multi-objective based optimization will be more suitable. Even where multi-objective optimization is employed, the involved systems fail to recognize that these real world problems are full of uncertainties. Type-2 fuzzy logic systems can handle the high level of uncertainties associated with the dynamic and changing environments, such as those presented with real world scheduling problems. This paper presents a novel multi-objective genetic type-2 Fuzzy Logic based System for the optimal allocation of mobile workforces to their working areas. The method has been applied in a real world service industry workforce environment. The results show strong improvements when the proposed multi-objective type-2 fuzzy genetic based optimization system was applied to the work area optimization problem as compared to the heuristic or type-1 single objective optimization of the work area. Such optimization improvements of the working areas will result in improving the utilization of the workforce.

In recent years, our homes have been equipped with smarter and more energy-efficient electric appliances, such as smart fridges, washing machines, TVs, etc. However, it seems that cookers seem to have been left aside during this trend although, for example, in U.K., electric cookers consume up to 20% of the evening peak electricity consumption. In addition, over half of the accidental house fires are due to cooking and cooking appliances. One of the reasons for the lack of smart energy-efficient electric cookers is the complexity of performing energy-efficient control for the various cooking techniques. This paper presents a fuzzy logic-based system, which can be cheaply retrofitted in existing electric cookers to convert them to semiautonomous, energy efficient, and safe smart electric cookers. The proposed system can control the cooker heating plate to allow the semiautonomous safe operation of the most common cooking techniques including boiling, stir/shallow-frying, deep-frying, and warming. In addition, the developed system can identify when human intervention is necessary and when dangerous situations happen or are imminent. We will present several real-world experiments, which were performed in the University of Essex intelligent apartment (iSpace) with various users where the proposed system operated a cooker semiautonomously in various cooking modes, and it was shown that when compared with the human manual operation, the proposed system realized an average energy saving of 21.42%, 34.43%, and 20.29% for the boiling, stir/shallow-frying, and deep-frying cooking techniques, respectively. In addition, the realized smart cooker has shown unique safety features not present in the existing commercial cookers.

The interval type-2 fuzzy Proportional-Integral (PI) controller (IT2-FPI) might be able to handle high levels of uncertainties to produce a satisfactory control performance, which could be potentially due to the robust performance as a result of the smoother control surface around the steady state. However, the transient state and disturbance rejection performance of the IT2-FPI may degrade in comparison with the type-1 fuzzy PI (T1-FPI) counterpart. This drawback can be resolved via general type-2 fuzzy PI controllers which can provide a tradeoff between the robust control performance of the IT2-FPI and the acceptable transient and disturbance rejection performance of the type-1 PI controllers. In this paper, we will present a zSlices-based general type-2 fuzzy PI controller (zT2-FPI), where the secondary membership functions (SMFs) of the antecedent general type-2 fuzzy sets are adjusted in an online manner. We will examine the effect of the SMF on the closed-system control performance to investigate their induced performance improvements. This paper will focus on the case followed in conventional or self-tuning fuzzy controller design strategies, where the aim is to decrease the integral action sufficiently around the steady state to have robust system performance against noises and parameter variations. The zSlices approach will give the opportunity to construct the zT2-FPI controller as a collection of IT2-FPI and T1-FPI controllers. We will present a new way to design a zT2-FPI controller based on a single tuning parameter where the features of T1-FPI (speed) and IT2-FPI (robustness) are combined without increasing the computational complexity much when compared with the IT2-FPI structure. This will allow the proposed zT2-FPI controller to achieve the desired transient state response and provide an efficient disturbance rejection and robust control performance. We will present several simulation studies on benchmark systems, in addition to real-world experiments that were performed using the PIONEER 3-DX mobile robot that will act as a platform to evaluate the proposed systems. The results will show that the control performance of the self-tuning zT2-FPI control structure enhances both the transient state and disturbance rejection performances when compared with the type-1 and IT2-FPI counterparts. In addition, the self-tuning zT2-FPI is more robust to disturbances, noise, and uncertainties when compared with the type-1 and interval type-2 fuzzy counterparts.

The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURSFA RC-HD) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results.

In this paper, we will present a wider view on the relationship between interval-valued fuzzy sets and interval type-2 fuzzy sets, where we will show that interval-valued fuzzy sets are a particular case of the interval type-2 fuzzy sets. For this reason, both concepts should be treated in a different way. In addition, the view presented in this paper will allow a more general perspective of interval type-2 fuzzy sets, which will allow representing concepts that could not be presented by interval-valued fuzzy sets.

Recent years have witnessed an expansion on realizing adaptive educational systems for intelligent E-learning platforms. Such platforms permit the development of customised learning contexts adapted to the requirements of every student by correlating the student characteristics with instructional variables. However, the vast majority of the existing adaptive educational systems do not learn from the users' behaviors to create white box models which could handle the linguistic uncertainties and could be easily read and analyzed by the lay user. Moreover, most of the existing systems ignore gauging the students' engagements levels and mapping them to suitable delivery needs which match the students' knowledge and preferred learning styles. This paper presents a novel interval type-2 fuzzy logic based system that can learn the users' preferred knowledge delivery needs and the preferred learning style based on the students' characteristics and engagement levels to generate a customized learning environment. The paper presents a novel system for gauging the students' engagement levels based on utilizing visual information to automatically calculate the engagement degree of students. This differs from traditional methods which usually employ expensive and invasive sensors. Our approach only uses a low-cost RGB-D video camera (Kinect, Microsoft) operating in a non-intrusive mode whereby the users are allowed to act and move without restrictions. The efficiency of the proposed system has been tested through various real-world experiments with the participation of 15 students. These experiments indicate the ability of the proposed type-2 fuzzy logic based system to handle the linguistic uncertainties to produce better performance in terms of improved learning and better user engagements when compared to type-1 based fuzzy systems and non-adaptive systems.

In this paper, we will explore whether the efficiency of the Interval Type-2 Fuzzy PID (IT2-FPID) lies in its ability to handle the high level of uncertainties rather than only having an extra degree of freedom provided by the Footprint of Uncertainty (FOU) on a highly nonlinear pH neutralization process. In order to illustrate the effect of the FOU on the control performance, the control performance of an IT2-FPID controller composed of 3×3 rules will be compared with a Type-1 Fuzzy PID (T1-FPID) controller of 5×5 rules. Moreover, in order to provide more extra degree of freedom to the T1-FPID structure, we will employ two self-tuning mechanisms where the weights of the fuzzy rules are adjusted in an online manner. Thus, we will present detailed comparative studies on how the extra degrees of freedom provided by the FOU or the employed tuning mechanisms affect the control and robustness performance. The presented analysis confirm that by tuning the FOU the performance of the IT2-FPID is better in wide range of operating points in comparison with its type-1 and self-tuning type-1 fuzzy counterparts which is not merely for the IT2-FPID use of extra parameters, but rather its different way of dealing with the disturbance, nonlinearities uncertainties and noise.

In this paper, we will compare the closed loop control performance of interval type-2 fuzzy PID controller with the type-1 fuzzy PID and conventional PID controllers counterparts for the Magnetic Levitation Plant. We will also compare the control performance of the interval type-2 fuzzy PID controller with the self-tuning type-1 fuzzy PID controllers. The internal structures of implemented controllers are firstly examined and then the design parameters of each controller are optimized for a given reference trajectory. The paper also show the effect of the extra degree of freedom provided by antecedent membership functions of interval type-2 fuzzy logic controller on the closed loop system performance. The real-time experiments are accomplished on an unstable nonlinear system, QUANSER Magnetic Levitation Plant, in order to show the superiority of the optimized interval type-2 fuzzy PID controller compared to optimized PID and type-1 counterparts.

Video monitoring can provide vital context awareness information from indoor intelligent environments where privacy is not a limitation. However, there is a need to develop linguistic summarization tools which are capable of summarizing in a layman language the information of interest within long video sequences. The key module which can enable the linguistic summarization of video monitoring is human activity/behaviour recognition. However, human behavior recognition is an important yet challenging task due to the behavior uncertainty, activity ambiguity, and uncertain factors such as position, orientation and speed, etc. In order to handle such high levels of uncertainties in activity analysis, we introduce a system based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) whose parameters are optimized by the Big Bang-Big Crunch (BB-BC) algorithm which allows for robust behaviour recognition using 3D machine vision techniques in intelligent environments. We present several experiments which were performed in real-world intelligent environments to fairly make comparisons with the state-of-the-art algorithms. The experimental results demonstrate that the proposed BB-BC paradigm is effective in tuning the parameters of the membership functions and the rule base of the IT2FLSs to improve the recognition accuracy. It will be shown through real-world experiments that the proposed IT2FLSs outperformed the Type-1 FLSs (TIFLSs) counterpart as well as other traditional non-fuzzy based systems. Based on the recognition results, higher-level applications will presented including video linguistic summarizations event searching and activity retrieval/playback.

Amongst Ambient Intelligence (AmI) services in the home environment is improving the quality of life through increasing the comfort levels of the users. One of the ways to meet user-friendly comfort requirements of an intelligent space is to establish more natural ways of interaction and hence a more serene communication between the humans and the intelligent systems. Inspired from the remarkable human mind, a trending research direction of fuzzy logic coined as 'Computing With Words' aims to link the computers and the users in a humanlike manner. In this video, we present fragments of lives of 3 different users and how an adaptive ambient intelligent platform for recommending recipes using Computing With Words (CWWs) helps enhance the comfort levels of users having diverse needs. The video also shows how this interaction is achieved by the CWWs framework components and the integration with a humanoid robot to promote assisted living in intelligent environments.

E-learning involves the computer and network-enabled transfer of skills and knowledge. The recent years have witnessed an increased interest in intelligent E-Learning platforms that incorporate adaptive educational systems which enable the creation of personalized learning environments to suit the students' individual requirements and needs. Such systems aim to correlate the student characteristics (such as knowledge level, personality and learning style) with instructional variables, (such as the presentation of learning materials and feedback). Various artificial intelligence based methodologies have been used to realize adaptive educational systems. However, the vast majority of the existing adaptive educational systems do not learn from the users' behaviors to create white box models which could be easily read and analyzed by the lay user. This paper presents a fuzzy logic based system that can learn the users' preferred knowledge delivery based on the students characteristics to generate a personalized learning environment. The proposed methodology employs a self-learning system which enables to generate a fuzzy logic based model from data. The fuzzy model is generated from data representing various students' capabilities and their desired learning needs. The learnt fuzzy based model is then used to improve the knowledge delivery to the various students based on their individual characteristics. The proposed system is adaptive where it is continuously adapting in a lifelong learning mode to make sure that the generated models adapt to the students individual preferences. We will present experiments carried with the proposed system which involved 17 students. The experiments will show how the proposed system learnt the students' preferences and created a model which allowed providing a personalized learning environment tailored according to the students' needs and requirements. This allowed improving the knowledge delivery which resulted in improving the students' performance.

Following the global economic crisis, many financial organisations around the World are seeking efficient frameworks for predicting and assessing financial risks. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyse a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modelling and prediction of financial applications. The proposed system is capable of generating summarized optimised type-2 FLSs based financial models which are easy to read and analyse by the lay user. The system is able to use the summarized model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains one for the prediction of good/bad customers in a credit card approval application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box financial models like the Evolving Decision Rule (EDR) procedure (which is based on Genetic Programming (GP) and decision trees) and gave a comparable performance to black box models like neural networks while the proposed system provided a white box model which is easy to understand and analyse by the lay user.

In this paper, we present an approach to interpret the Computing With Words (CWWs) paradigm merging the advancements from neuroscience, psychology and artificial intelligence. The presented approach will incorporate fuzzy composite concepts (FCCs), a special case of linguistic weighted average (LWA) and case-based reasoning (CBR). The focus of the paper is on the inception of the CWWs paradigm to bridge the gap between the human and machine intelligence. The investigation of FCCs processing is performed using linear general type-2 (LGT2) and interval type-2 (IT2) fuzzy sets. The results show that LGT2 fuzzy sets outperform IT2 fuzzy sets in the processing time of complete rule base evaluation, in providing better modeling of the human perceptual judgment, and in producing richer range of output intervals.

This paper aims to investigate the development of an intelligent refrigerator through embedding intelligent agents in normal refrigerators. The proposed intelligent refrigerator will be able to recognize various food items and learn user consumption habits via the proposed intelligent agent. The system incorporates a low-cost camera which feeds its input to the fuzzy agent in order to classify different food items and track their amounts. Using this information, consumption patterns representing the amount of items taken by users are generated and fed into a neural network system which is aimed to learn user habits and provide feedback to the user in cases where he/she consumes an unusual number of items. The system employs a second camera to distinguish between different users so that user specific information such as items consumed and calories taken can be stored separately. The resulting system has an accuracy of ≃ 90% for item identification and shows a very good performance for learning the habits of different users. The system also provides a graphical interface to display available items which allows users to generate an automated shopping list.

Among all domestic appliances populating our homes, it seems that hobs are not yet enabled with intelligent and energy efficient systems where people still use hobs in the same way their parents or grandparents did. This work aims to develop autonomous cooking systems which are able to provide a better cooking practice while increasing the energy efficiency of existing hobs. In this paper, we will present fuzzy logic based agents which could be embedded in normal hobs converting them to intelligent and more energy efficient ones. The realised system is able to self-regulate the heating during the most widely used cooking techniques including boiling, deep-frying, stir-frying, warming and melting. The agent is also capable of identifying when the user intervention is needed where the system continuously controls the cooking pot temperature preventing it from reaching unwanted values and detects when a pan boils-dry switching the appliance off immediately. We will present various real world experiments where it will be shown that the developed intelligent hob is capable of performing autonomous cooking to the satisfaction of the human user(s) while providing an energy saving of around 15-20% when compared to the human cooking practice.

In this paper, we will present a big bang-big crunch optimization (BB-BC) based approach for the design of an interval type-2 fuzzy PID controller. The implemented global optimization algorithm has a low computational cost and a high convergence speed. As a consequence, the BB-BC method is a very efficient search algorithm when the number of the optimization parameters is relatively big. The optimized type-2 fuzzy controller is compared with PID and type-1 fuzzy PID controllers which were optimized with either the BB-BC optimization method or conventional design strategies. The paper will also show the effect the extra degrees of freedom provided by the antecedent interval type-2 fuzzy sets on the closed loop system performance. We will present a comparative study performed on the highly nonlinear cascaded tank process to show the superiority of the optimized interval type-2 fuzzy PID controller compared to its optimized PID, type-1 counterparts.

Within service providing industries, one of the challenges facing resource planners is to match the demand for services by trying to utilize the available resources as best as possible. The problem faced by the operational resource planner is to build a refined plan of tasks to resources for each day in a manner that the plan can be directly dispatched to the distributed available engineering field force. In this paper, we will introduce a genetic hierarchical interval type-2 fuzzy logic based operational planner. We will present experiments which will show that the proposed system is able to produce more efficient plans when compared to the traditional crisp logic based algorithms which employ hill climbing heuristic based search techniques. We will show also that the proposed system outperforms the type-1 fuzzy logic based counterparts.

Decision making could be viewed to include Multi-Criteria Group Decision Making (MCGDM). MCGDM is a decision tool which it is able to find a unique agreement from number of decision makers/users by evaluating the uncertain judgment among them. Several fuzzy logic based approaches have been employed in MCGDM to handle the linguistic uncertainties and hesitancy. However, there is a need to handle the high level of uncertainties that exist in decision making problems involving numbers of decision makers/experts/users with varying points of view. In this paper, we present a general type-2 fuzzy logic based approach for MCGDM. The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers' (DMs) judgments and the vagueness of the appraisal. The proposed method utilizes general type-2 fuzzy sets. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs' opinions into a unique approval. We will present results from the proposed system deployment for the assessment of the postgraduate study. The proposed system was able to model the variation in the group decision making process exhibited by the various decision makers' opinions. In addition, the proposed system showed agreement between the proposed method and the real decision outputs from DMs (as quantified by the Pearson Correlation) which outperformed the MCGDM systems based on type-1 fuzzy sets, interval type-2 fuzzy sets and interval type-2 fuzzy sets with hesitation index.

One of the key components in the development of intelligent environments is the recognition and analysis of human behaviour. However, the majority of traditional non-fuzzy machine vision based approaches rely on assumptions such as known spatial locations and temporal segmentations or they employ computationally expensive approaches such as sliding window search through a spatio-temporal volume. Hence, it is difficult for such traditional non-fuzzy methods to scale up and handle the high-levels of uncertainties available in real-world applications. This paper presents a system which is based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) for robust human behaviour recognition using machine vision in intelligent environments. We will present several experiments which were performed on the publicly available Weizmann human action dataset. It will be shown that the proposed IT2FLS outperformed the Type-1 FLS (T1FLS) counterpart as well as outperforming other traditional non-fuzzy systems.

E-learning involves the computer and network-enabled transfer of skills and knowledge. The internet has become a central core to the educative environment experienced by learners, hence facilitating learning at any location and at any time thus creating pervasive learning environments. There is a growing interest in developing e-Learning platforms which enable the creation of personalized learning environments to suit the students' individual requirements and needs. However, the vast majority of the existing adaptive educational systems do not learn from the users' behaviors to create white box models which could handle the linguistic uncertainties and could be easily read and analyzed by the lay user. This paper presents a type-2 fuzzy logic based system that can learn the users' preferred knowledge delivery based on the students characteristics to generate a personalized learning environment. The type-2 fuzzy model is first created from data acquired from a number of students with different capabilities and needs. The learnt type-2 fuzzy-based model is then used to improve the knowledge delivery to the various students based on their individual characteristics. We will show how the presented system enables customizing the learning environments to improve individualized knowledge delivery to students which can result in enhancing the students' performance. The proposed system is able to continuously respond and adapt to students' needs on a highly individualized basis. Thus, online courses can be structured to deliver customized education to the student based upon various criteria of individual needs and characteristics. The efficiency of the proposed system has been tested through various experiments with the participation of 17 students. These experiments indicate the ability of the proposed type-2 fuzzy logic based system to handle the linguistic uncertainties to produce better performance than the type-1 based fuzzy systems.

One of the challenges for the fast advancing ambient intelligence vision is to maintain the perception of the future home being a safe place where the inhabitants relax, enjoy and feel comfortable. In a home environment, the role of technology is approached skeptically, hence, there is a need to provide high-level communication between the users and the intelligent space so that the users get accustomed to what technology has to offer. In this paper, we introduce a Computing with Words (CWWs) framework which provides human-like reasoning via abstraction and high-level description of thoughts, feelings, etc. This framework can be considered as an exocortex as it aids the human thinking outside the bio-brain. The CWWs paradigm aims to establish high level home-human communication, which is necessary for people to perceive the technology as a cooperative guide and an improvement on their life styles.

The real-time path tracking control of mobile robots attracted considerable research interest since they inherit non-holonomic properties and uncertainties caused by the internal dynamics and/or feedback sensors. In this paper, we will propose a cascade control architecture, which includes the inner and outer control loops, for the path tracking control of mobile robots. In the proposed mobile robot cascade structure, interval type-2 fuzzy PID controllers are implemented as the outer and inner loop controllers to achieve a satisfactory tracking performance in presence of uncertainties. In this context, we will present a simple two stage mobile robot cascade design strategy. We will present real-time control experiments performed on the PIONEER 3-DX mobile robot to show the efficiency and the superior tracking performance of the type-2 fuzzy cascade control architecture in comparison with its conventional PID and type-1 fuzzy controllers counterparts in presence of uncertainties.

Human behaviour recognition systems hold the possibility of performing a variety of important assistive and management tasks in the development of ambient intelligent environments. However, the traditional non-fuzzy approaches for behaviour recognition using machine vision mostly rely on the assumptions such as known spatial locations and temporal segmentations or indispensably employ computationally expensive approaches such as sliding window search through a spatio-temporal volume. Hence, it is difficult for such traditional non-fuzzy methods to scale up the intelligent environments and handle the high-level of uncertainties available in real-world applications. To address these problems, this paper presents a system which is based on Interval Type-2 Fuzzy Logic Systems (IT2FLSs) whose parameters are optimized by the Big Bang-Big Crunch (BB-BC) algorithm which allows for robust behaviour recognition using machine vision in intelligent environments. We will present several experiments which were performed on the publicly available Weizmann human action dataset to fairly compare with the state-of-the-art algorithms. The experimental results demonstrate that the proposed optimization paradigm is effective in tuning the parameters of the membership functions and the rule base of the IT2FLSs to improve the recognition accuracy where the proposed IT2FLSs outperformed the Type-1 FLSs (TIFLSs) counterpart as well as outperforming other traditional non-fuzzy systems.

Singleton interval type-2 fuzzy logic systems (FLSs) have been widely applied in several real-world applications, where it was shown that the singleton interval type-2 FLSs outperform their singleton type-1 counterparts in applications with high uncertainty levels. However, one of the main criticisms of singleton interval type-2 FLSs is the fact that they outperform singleton type-1 FLSs solely based on their use of extra degrees of freedom (extra parameters) and that type-1 FLSs with a sufficiently large number of parameters may provide the same performance as interval type-2 FLSs. In addition, most works on type-2 FLSs only compare their results with singleton type-1 FLSs but fail to consider nonsingleton type-1 systems. In this paper, we aim to directly address and investigate this criticism. In order to do so, we will perform a comparative study between optimized singleton type-1, nonsingleton type-1, and singleton interval type-2 FLSs under the presence of noise. We will also present a multiobjective evolutionary algorithm (MOEA) for the optimization of singleton type-1, nonsingleton type-1, and singleton interval type-2 fuzzy systems for function approximation problems. The MOEA will aim to satisfy two objectives to maximize the accuracy of the FLS and minimize the number of rules in the FLS, thus improving its interpretability. Furthermore, we will present a methodology to obtain “optimal” consequents for the FLSs. Hence, this paper has two main contributions: First, it provides a common methodology to learn the three types of FLSs (i.e., singleton type-1, nonsingleton type-1, and singleton interval type-2 FLSs) from data samples. The second contribution is the creation of a common framework for the comparison of type-1 and type-2 FLSs that allows us to address the aforementioned criticism. We provide details of a series of experiments and include statistical analysis showing that the type-2 FLS is able to handle higher levels of noise than its nonsingleton and singleton type-1 counterparts.

Ambient intelligence is a new information paradigm, where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive, and responsive to their needs. Hence, one of the important requirements for ambient intelligent environments (AIEs) is the ability to localize the whereabouts of the user in the AIE to address her/his needs. In order to protect user privacy, the use of cameras is not desirable in AIEs, and hence, there is a need to rely on nonintrusive sensors. There are various localization means that are available for outdoor spaces such as those which rely on satellite signals triangulation. However, these outdoor localization means cannot be used in indoor environments. The majority of nonintrusive and noncamera-based indoor localization systems require the installation of extra hardware such as ultrasound emitters/antennas, radio-frequency identification (RFID) antennas, etc. In this paper, we propose a novel indoor localization system that is based on WiFi signals which are free to receive, and they are available in abundance in the majority of domestic spaces. However, free WiFi signals are noisy and uncertain, and their strengths and availability are continuously changing. Hence, we present a fuzzy logic-based system which employs free available WiFi signals to localize a given user in AIEs. The proposed system receives WiFi signals from a large number of existing WiFi access points (up to 170 access points), where no prior knowledge of the access points locations and the environment is required. The system employs an incremental lifelong learning approach to adjust its behavior to the varying and changing WiFi signals to provide a zero-cost localization system which can provide high accuracy in real-world living spaces. We have compared our system in both simulated and real environments with other relevant techniques in the literature, and we have found that our system outperforms the other systems in the offline learning process, whereas our system was the only system which is capable of performing online learning and adaptation. The proposed system was tested in real-world spaces from a living lab intelligent apartment (iSpace) to a town center apartment to a block of offices. In all these experiments, our system has been highly accurate in detecting the user in the given AIEs, and the system was able to adapt its behavior to changes in the AIE or the WiFi signals. We envisage that the proposed system will play an important role in AIEs, especially for privacy concerned situations like elderly care scenarios.

Ambient Intelligence (AmI) is a vision that refers to an information technology paradigm where a physical environment is `aware' of its human occupants' presence/context and is sensitive, adaptive and responsive to their needs. Physical environments that are augmented with AmI are called Ambient Intelligent Environments (AIEs) which are deemed to be intelligent because the system should be able to recognise human occupants, reason with context and program itself to meet the occupants' needs by learning from their behaviour [1]. However, there is a need also to deal with real-world scenarios which involve multiple users occupying a given AIE. In order to handle multi-user AIEs and control them, there is a need to have agents that are able to learn the user(s) behaviours and handle the intra and inter-user uncertainties as people have different preferences and profiles which continuously change. In this paper, we present a zSlices based type-2 fuzzy agent which employs zSlices general type-2 fuzzy systems to learn the user(s) preferences and profiles and handle the encountered intra and inter-user uncertainties. The agent will behave according to a learned user profile that is unique to an individual user or a group of users and so the profile-selection problem manifests when the set of users in an AIE changes (i.e. when people enter/ leave an AIE). The proposed agent employs a novel strategy that we call Dynamic Profile-Selection that uses a cloud-based profile repository in order to support the agent activity in multiple AIEs. To demonstrate the proposed approach, we have conducted real-world experiments on two distinct AIEs which are the intelligent apartment (iSpace) and the intelligent Classroom (iClassroom) located at the University of Essex.

The concept of Computing With Words (CWW) was coined by Zadeh to be a methodology in which words are used instead of numbers for computing and reasoning. Since then, there have been various angles to interpret CWW. However, there is a need to tackle the problem of modeling a `word' by fuzzy sets, which is one of the building blocks of the CWW concept. In this paper, we investigate the `word' from the perspective of the parts of speech in English language. We point out that there exists a hierarchical analogy between the parts of speech and a linguistic variable in a fuzzy system. In other words, the linguistic variable in a fuzzy system can be interpreted to be a noun whereas the corresponding linguistic labels quantifying the linguistic variable can be classified as being `qualifiers + adjectives'. We propose to model the linguistic uncertainty conveyed by qualifiers as second-order word uncertainty using a general type-2 fuzzy set based approach where the qualifiers can be exploited in linear terms. In particular, we suggest a linear representation of the third dimension of a general type-2 fuzzy set where we consider only left and right shoulder membership functions. We show that the interpretation of the paradigm using linear adjectives simplifies the comprehension of the third dimension as well as offering a way to avoid the shortcomings of type-1 and interval type-2 fuzzy sets in modeling a word for CWW. For illustration, we present examples comparing the interval type-2 fuzzy labels (which are greater in number) and the linear general type-2 (LGT2) fuzzy labels which provide an efficient management of the linguistic variable by revealing a potential to reduce complexity.

One of the important requirements for Ambient Intelligent Environments (AIEs) is the ability to localise the whereabouts of the user in the AIE to address her/his needs. The outdoor localisation means (like GPS systems) cannot be used in indoor environments. The majority of non intrusive and non camera based indoor localisation systems require the installation of extra hardware such as ultra sound emitters/antennas, RFID antennas, etc. In this paper, we will propose a novel fuzzy logic based indoor localisation system which is based on the WiFi signals which are free to receive and they are available in abundance in the majority of domestic spaces. The proposed system receives WiFi signals from a big number of existing WiFi Access Points (up to 170 Access Points) with no prior knowledge of the access points locations and the environment. The proposed system is able to adapt online incrementally in a lifelong learning mode to deal with the uncertainties and changing conditions facing unknown indoor structures with a few days of calibration at zero-cost deployment with high accuracy. The proposed system was tested in simulated and real environments where the system has given high accuracy (that outperformed the existing techniques) to detect the user in the given AIE and the system was able also to adapt its behaviour to changes in the AIE or the WiFi signals.

Workforce management is one of the most important factors in the success of any company that provides its customers with services. Hence, in order for the company to achieve objectives like customer satisfaction and maximum resource utilization, there is a need to have a reliable means of efficiently managing the company workforce and making sure that the produced plan always gives a good choice when it comes to assigning the available technicians to the given jobs. As the quantity of services and the workforce grow, the use of an automated workforce management system becomes inevitable. However the automated workforce management system should allow full transparency to allow the user to interact with the generated plans. In addition, the workforce management systems face high levels of uncertainties when dealing with real-world scenarios, which necessitates employing systems, which are able to handle the linguistic and numerical uncertainties available in the real-world scenarios. Fuzzy Logic Systems (FLSs) are credited with providing transparent methodologies that can deal with the imprecision and uncertainties. However the vast majority of the FLSs employ the type-1 FLSs, which cannot directly handle the high levels of uncertainties. Type-2 FLSs which employ type-2 fuzzy sets can handle such high levels of uncertainties to give very good performances. In this paper, we will present a type-2 FLS based workforce management system that is being developed for a delivery unit in British Telecom (BT). We will show how the presented system was able to handle the faced uncertainties to give very good performance that outperformed the automated non-intelligent system and the type-1 FLSs based system.

Multi-Criteria Decision Making (MCDM) aims to develop techniques that are able to make decisions and solve complex problems where the outcome is a factor of various conflicting criteria. Intuitionistic Fuzzy Sets (IFSs) have been shown to provide a suitable framework for dealing with decision-making systems involving membership, non-membership and hesitation which showed very good results when dealing with conflicting criteria. On the other hand, Group Decision Making (GDM) deals with decision-making systems which need to consider the opinions of a group of experts whose decisions and opinions are subject to linguistic uncertainties. Previous research has shown the power of interval type-2 fuzzy logic systems to handle the linguistic uncertainties in decision-making systems. In this paper, we propose a hybrid method combining interval type-2 fuzzy logic and IFSs to develop a Multi-Criteria Group Decision Making (MCGDM) system. The intuitionistic evaluation in interval type-2 membership functions has been derived from the proposed method which includes eight steps for the aggregation and ranking of the preference alternatives. We will present results from the proposed system deployment for the assessment of the postgraduate study where the evaluation involved 10 candidates. The proposed system was able to model the variation in the group decision-making process exhibited by the various decision-makers' opinions. In addition, the proposed system was able to provide a better agreement with human decisions compared to IFS, type-1 and interval type-2 fuzzy systems.

An interpretation of the state of health of the baby can be inferred through assessment of the umbilical cord acid-base (UAB) status. This assessment can be made based on pH and other parameters from both arterial and venous blood from the newborn umbilical cord. This can distinguish the cause of a low pH between the distinct physiological conditions of respiratory acidosis due to a short-term accumulation of CO2and a metabolic acidosis (low pH in the tissues) due to lactic acid from a longer-term oxygen deficiency. This UAB assessment suffers the problem of high uncertainty levels between the various experts. Hence, researchers have tried to develop computer based models for the assessment of UAB. Previous research has shown the power of fuzzy logic systems to provide frameworks to handle the encountered uncertainties in real decision making models. Fuzzy Multi-criteria Group Decision Making (MCGDM) has been shown to be an efficient technique for obtaining rankings from experts' opinions. This paper presents a fuzzy logic based multi-criteria group decision making system for the assessment of umbilical cord acid-base. The proposed system models the variation in the decision making process exhibited by the various experts. We will present results which show how the proposed system can give a better agreement with the experts compared to an existing fuzzy expert system (FES).

This paper addresses the problem of learning human behavior models from sensor information in a smart home environment. Any smart home is provided with many devices that can determine the state of the environment at any moment, as well as the user interaction with the environment. This information is used by our approach to learn a flexible and reliable human behavior representation, extracting the relevant actions and the order constraints among them. In order to test our learning approach, we have performed experiments in the iSpace at the University of Essex which is an Ambient Intelligent Environment (AIE) testbed. We will present the results obtained by monitoring three participants' activities for three specific behaviors. The learned behavior model is compared with the behavior model provided by the participants. The results show that our proposed system effectively learns the behavior models for any behavior, acquiring not only the actions the user considers as basic, but also those unconsciously performed yet important ones done by the user.

Fuzzy logic Systems (FLSs) are credited with providing very good performances which are able to handle the uncertainty and imprecision present in real-world environments and applications. Using type-2 FLSs can enable handling higher levels of uncertainty when compared to type-1 FLSs. The majority of the type-2 FLSs employ singleton type-2 FLSs which handle the encountered input uncertainty through fuzzy sets representing the linguistic labels in the antecedent fuzzy sets. However, singleton type-2 FLSs assume that the input signal is perfect and thus there is no provision for handling the uncertainties in the incoming input signals. Hence, there have been some efforts to investigate non-singleton type-2 FLS. However, the papers that employed non-singleton type-2 FLSs assumed that the fuzzy inputs are having a predefined shape (mostly Gaussian) which might not model the encountered uncertainty properly. In our previous works, we presented adaptive type-2 input based non-singleton type-2 FLS which employs dynamic inputs which are not assuming any specific shape. We have shown how the adaptive type-2 input based non-singleton type-2 FLS outperforms singleton (type-1 and type-2) FLSs. In this paper, we will compare the adaptive type-2 input based non-singleton type-2 FLS with other non-singleton (type-1 and type-2) FLSs which employ Gaussian fuzzy inputs. We will present real-world robot experiments showing how the adaptive type-2 input based non-singleton type-2 FLS outperforms the non-singleton FLSs which employ Gaussian fuzzy inputs when large amounts of uncertainty are encountered.

Classical ontology is not sufficient to deal with vague or imprecise knowledge for real world applications such as malware behavioral analysis. In addition, malware has grown into a pressing problem for governments and commercial organizations. Anti-malware applications represent one of the most important research topics in the area of information security threat. As a countermeasure, enhanced systems for analyzing the behavior of malware are needed in order to predict malicious actions and minimize computer damages. Many researchers use Virtual Machine (VM) systems to monitor malware behavior, but there are many Anti-VM techniques which are used to counteract the collection, analysis, and reverse engineering features of the VM based malware analysis platform. Therefore, malware researchers are likely to obtain inaccurate analysis from the VM based approach. For this reason, we have developed the Taiwan Malware Analysis Net (TWMAN) which uses a real operating system environment to improve the accuracy of malware behavior analysis and has integrated Type-1 Fuzzy Set (T1FS), Ontology, and Fuzzy Markup Language (FML) on 2010. In this paper, we use Interval Type-2 Fuzzy Set (IT2FS), eggdrop, and glftpd as a cloud service (software as a service) on the Google App Engine along with Python and Android. We believe this system can help improve the correctness of malware analysis results and reduce the rate of malware misdiagnosis.

A Fuzzy Logic System (FLS) is generally credited with being an adequate methodology for real world applications which are subject to high uncertainty levels. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed nonsingleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In our previous work, we have presented some of the theoretical basis for generating an adaptive type-2 fuzzy input which is better able to represent the encountered uncertainty at a given measurement. The nonsingleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the uncertainty distribution associated with the given sensor. In this paper, we will present an overview on how the adaptive type-2 input based nonsingleton interval type-2 FLS can operate in real time. We will present real world experiments using a mobile robot which will show how under high input uncertainty levels, the nonsingleton type-2 FLS can give a good performance and outperform its singleton type-2 and type-1 FLSs counterparts.

Computing with Words (CWW) aims to investigate the possibility of imitating the unique ability of humans for approximate reasoning on the basis of approximately defined classes and concepts in the form of words. Type-2 fuzzy sets have been used to provide an adequate modeling basis for words in a fuzzy logic context. In the context of type-2 fuzzy sets employed as part of CWW, a variety of research efforts have been made to investigate approaches to model the meaning of specific words using type-2 fuzzy sets. In this paper we start by focusing on the interpretation of classical set-theoretical operations (complement, union and intersection) for crisp and type-1 fuzzy sets. We proceed by extending the interpretations to the results of the union and intersection operations of interval type-2 fuzzy sets, specifically indicating their effect on the uncertainty representation in the sets. We note the impact of the choice of t-norms and t-conorms in particular in the context of CWW applications where the interpretation of the resulting sets and its resemblance to the human intuitive meaning of the concept or word is essential. Finally, we provide the interpretation and reasoning behind the Multi Level Agreement (MLA) operation based on zSlices which was previously introduced and discuss the requirement for the selection of the right operations for the amalgamation of individual fuzzy sets and the potential for investigating this choice in particular in a CWW context.

Ubiquitous Computing asserts that technology will soon be pervasive in our lives. But how will that technology be organized and made available to us as we roam through the many spaces of our daily lives? How will we see and use that which we have a right to, and more importantly, how will a lack of access rights be enforced? These are questions that have been raised following previous research projects. They become more significant as the concept of intelligent environments scales-up beyond the boundaries of four walls. In this paper we propose a formal model that will form a roadmap for some of our upcoming research. This paper is a hypothetical work that explores some questions and poses some answers - Influenced by experimentation and with a view to further investigation.

Activity recognition research relies heavily on test data to verify the modeling technique and the performance of the activity recognition algorithm. But data from real deployments are expensive and time consuming to obtain. And even if cost is not an issue, regulatory limitations on the use of human subjects prohibit the collection of extensive datasets that can test all scenarios, under all circumstances. A powerful and verifiable simulation tool is needed to accelerate research on human activity recognition. We present Persim, an event driven simulator of human activities in pervasive spaces. Persim is capable of capturing elements of space, sensors, behaviors (activities), and their inter-relationships. We focus on presenting the five main use cases for Persim addressing dataset synthesis, reuse and extension of existing datasets, sharing of data and simulation projects, as well as data validation.

In this paper, we will present an intelligent fuzzy logic based system for market agents operating within the Trading Agent Competition's Market Design game. The objective of this competition is to design intelligent agents and systems that would be able to manage an electronic double auction market effectively in competition with other markets for market share and profit. Thus the market share and profit along with the market's transaction success rate constitute the agent's score in the game. Managing the market involves setting rules for accepting, matching and clearing offers as well as pricing transactions and imposing fees. In this paper, we will present our agent which is called MyFuzzy which employs a fuzzy logic based module to impose its fees in order to balance the profit and market share. The novelty of this approach lies in the fact that it makes minimal assumptions about the nature of the game environment by using fuzzy techniques to adapt the agent behaviour by recognizing the market context. This allowed our agent to achieve superior performance to the other existing techniques as it avoids factors that other agents overlook. Furthermore, we introduce novel modifications of existing pricing and accepting techniques to work in conjunction with our charging module. The proposed agent has been compared against the other agents (including the winners of the 2009 trading agents competition) where our agent has achieved superior performance.

The generation of useful sensory data from real-world deployments of Ambient Intelligent Environments (AIEs) is challenging because of the high cost, significant groundwork and lack of access to human subjects. This situation can be improved by providing efficient simulators that can produce realistic simulation of the data collection from AIEs. One of the main problems for developing AIE simulators lies in the ability to verify how close the simulated data are to the real world data. In this paper, we present a fuzzy based verification agent for Persim - an event driven simulator for human activities in AIEs. The employed fuzzy based verification agent builds a data model that mimics the operation of Persim which allows for the latter's objective and subjective verification. We have conducted the verification on real world data captured from an actual smart apartment deployment. The results show the effectiveness of the fuzzy based verification agent in analyzing and comparing the Persim simulated data with the real world collected data. We also demonstrate how the verification agent is able to pinpoint specific changes to the simulation model to increase the realism of the simulation.

This paper presents the system used by the team of the German University in Cairo (GUC) within the FESTO Hockey Challenge league that took place within RoboCup 2009. The goal of the FESTO Hockey Challenge is to have a competition between robotic teams where each team consists of three robots to compete in an Ice Hockey game. All robots are of the same mechanical, sensor and electronic capabilities so that the focus of the competition is to develop novel artificial intelligence techniques for robot control and coordination. The GUC team scored the 2ndplace in this competition after losing by penalty shoot outs in the final. The proposed control approach for GUC team employed Hierarchical Fuzzy Logic Controllers (HFLCs) in which the low level behaviours are implemented using FLCs and the coordination between the behaviours is implemented by a high level fuzzy layer. The coordination between the robotic agents team members is implemented by a hierarchical situation based dynamic role allocation mechanism. The paper will describe the employed approaches and will report on the results achieved.

This paper investigates the performance of a lead-acid battery in a grid connected wind energy generator system. Wind energy has gained much credit in the past two decades as a sustainable energy resource. The penetration of wind energy generators into the electric utility grids is expected to increase to about 1.5 TW within the present decade. Due to the intermittent nature of the wind, there have been serious concerns about reliability and operation of the utility power grids. Battery storage is suggested to compensate wind power fluctuations and smooth the power fed to the utility grids. The battery storage in such applications has dynamic operating conditions and is subjected to different ageing mechanisms which stimulate the capacity degradation and hence influence the feasibility of their implementation. This paper investigates the implementation of fuzzy agent modeling as a powerful technique to estimate the dynamic and sophisticated electrochemical battery degradation mechanisms. Accordingly, the real behavior, the feasibility of the battery and its effect on wind power fed to the utility grid can be judged. The investigated system is simulated using real measurement data of a 600 kW rated power wind turbine. The simulation results of different battery capacities show that the integration of the battery storage has compensated the fluctuations of the generated wind power and smoothed the power fed to the utility grid. Moreover, the fuzzy agent has generated very important information about the battery degradation and available capacity (in this case of about 85%) after one year of operation.

In this paper, we present a novel technique to generate zSlices based general type-2 fuzzy sets using a series of interval type-2 fuzzy sets based around the notion of "agreement" of interval type-2 fuzzy sets. We provide details on how this approach can be applied for a series of readily available interval type-2 fuzzy sets as well as how the proposed approach can be employed to generate zSlices based general type-2 fuzzy sets which are continually updated as new interval type-2 fuzzy sets become available over time. We also describe the proposed approach in the context of Ambient Intelligent Environments (AIEs) which illustrate the benefits of a continuously updated general type-2 membership function and its potential advantage over interval type-2 fuzzy logic based approaches. Subsequently, we demonstrate the approach based on triangular interval type-2 fuzzy sets and we highlight the remaining complexities and complications in terms of the implementation of the proposed technique which stem from the potential for the creation of non-convex fuzzy sets and propose solutions for these problems.

The diminishing reserves of fossil fuels together with the associated environmental effects is encouraging the transition to renewable clean energy. Due to this transition, improvements took place in numerous fields related to wind energy generation. To cope with those improvements, the need emerged to develop intelligent control mechanisms that can handle the uncertainties encountered in wind turbines. In this paper we present a novel type-2 fuzzy logic system that models wind turbines to accurately predict the extracted power. Fuzzy models in this paper were generated using data and adapted to deal with noise. The type-2 fuzzy based models were compared against the corresponding type-1 fuzzy models. Although type-1 returns precise results under ideal conditions, it cannot deal with any encountered uncertainties unlike the type-2 fuzzy model that is able to handle the encountered uncertainties to give a better model.

As the Home Area Network (HAN) evolves, there is an increase in both the number and diversity of device deployment. This includes embedded devices whose resource constraints do not permit the efficient performance of high level middleware functionality. We herein present the functionality and knowledge representations required to enable such “simpleware” devices to be dynamically represented by proxy within our Nexus middleware framework. We also present a use case to illustrate the proposed solution.

In this short conceptual paper we explore the need for demonstrations of intelligent environments research that can convey what we as researchers think to potential users that have limited exposure to such ideas. This is especially important where physical and virtual worlds meet in the smart home context. We present several exemplars that are intended to promote user understanding through the use of mixed reality technologies.

The widespread of embedded computer networks as part of everyday peoples' lives is leading the current research towards smart environments and Ambient Intelligence (AmI). AmI is a new information paradigm where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive and responsive to their needs. In this paper, we describe the intelligent Classroom (iClass) which aims to realize the AmI vision in Education in universities and schools. We will describe the architecture employed to build the iClass and we will present three different directions including the utilization of RFID technology, interacting with the user via speech and developing intelligent agents to learn the user behavior and adapt to its change over short and long time intervals.

Wind energy is becoming one of the most important and promising areas of renewable energy. During the past few years, wind energy generation underwent strong improvements in several fields including power electronics, mechanics, wind dynamics, etc. However, there is a high need to develop more intelligent control mechanisms that can handle the various sources of uncertainties encountered in wind turbines and allow maximum power to be obtained from wind. The recent years have witnessed the use of type-2 fuzzy logic systems to generate controllers which are able to provide robust control performances in the face of high levels of uncertainty. This paper presents a method to generate a type-2 fuzzy logic model entirely from data to provide a dynamic footprint of uncertainty for the generated fuzzy set. The fuzzy model will be used to predict the wind speed experienced by a wind turbine without the use of sensors. This estimated wind speed is then passed for another fuzzy controller that changes the pitch angles of the wind turbine blades in order to track the maximum power available.

The importance of ubiquitous environments has increased in recent years as it has been recognized as a paradigm that can improve the quality of life of many sectors of the population especially care of elderly people by providing automated environments that adapt and respond to its inhabitants' needs. The aim of the work presented here is to provide a solution to the problem of recognition and detection of human behaviours inside ubiquitous environments by using a neural-network driven embedded agent working with online, real-time data from a network of unobtrusive low-level sensors. The final objective of this system was to classify a ‘normal’ pattern of activities, and sense deviations from it, which could be employed for home care applications.

Nowadays most people can get enough energy to maintain one-day activity, while few people know whether they eat healthily or not. It is quite important to analyze nutritional facts for foods eaten for those who are losing weight or suffering chronic diseases such as diabetes. This paper proposes a novel type-2 fuzzy ontology, including a type-2 fuzzy food ontology and a type-2 fuzzy markup language (FML)-based ontology, for diet assessment. In addition, we also present a type-2 FML (FML2) to describe the type-2 fuzzy ontology and the FML2-based diet assessment agent, including a type-2 knowledge engine, a type-2 fuzzy inference engine, a diet assessment engine, and a semantic analysis engine. In the proposed approach, first, the nutrition facts of various kinds of food are collected from the Internet and the convenience stores. Next, the domain experts construct the type-2 fuzzy ontology, and then the involved subjects are requested to input the different food eaten. Finally, the proposed FML2-based diet assessment agent displays the diet assessment of the food eaten based on the constructed type-2 fuzzy ontology. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively where the proposed system can provide a diet health status, which can act as a reference to promote healthy living. © 2010 Wiley Periodicals, Inc.

This article presents a novel intelligent embedded agent approach for reducing the number of associations and interconnections between various agents operating within ad hoc multiagent societies of an Ambient Intelligent Environment (AIE) in order to reduce the processing latency and overheads. The main goal of the proposed fuzzy-based intelligent embedded agents (F-IAS) includes learning the overall network configuration and adapting to the system functionality to personalize themselves to the user needs based on monitoring the user in a lifelong nonintrusive mode. In addition, the F-IAS agents aim to reduce the agent interconnections to the most relevant set of agents in order to reduce the processing overheads and thus implicitly improving the system overall efficiency. We employ embedded ambassador agents, namely embassadors, which are designated F-IAS agents utilized with additional novel characteristics to not only act as a gateway filtering the number of messages multicast across societies but also discover, recommend, and establish associations between agents residing in separate societies. In order to validate the efficiency of the proposed methods for multiagent and society-based intelligent association discovery and learning of F-IAS agents/embassadors we will present two sets of unique experiments. The first experiment describes the obtained results carried out within the intelligent Dormitory (iDorm) which is a real-world testbed for AIE research. Here we specifically demonstrate the utilization of the F-IAS agents and discuss that by optimizing the set of associations the agents increase efficiency and performance. The second set of experiments is based on emulating an iDorm-like large-scale multi-society-based AIE environment. The results illustrate how embassadors discover strongly correlated agent pairs and cause them to form associations so that relevant agents of separate societies can start interacting with each other.

It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. TheT2FOis composed of 1) atype-2 fuzzy personal profile ontology(type-2 FPPO); 2) atype-2 fuzzy food ontology(type-2 FFO); and 3) atype-2 fuzzy-personal food ontology(type-2 FPFO). In addition, the paper also presents aT2FS-based intelligent diet-recommendation agent(IDRA), including 1)T2FSconstruction; 2) aT2FS-based personal ontology filter; 3) aT2FS-based fuzzy inference mechanism; 4) aT2FS-based diet-planning mechanism; 5) aT2FS-based menu-recommendation mechanism; and 6) aT2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs aT2FO, including atype-2 FPPO, atype-2 FFO, and a set oftype-2 FPFOs. Finally, theT2FS-based IDRAretrieves the builtT2FOto recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts.

Higher order fuzzy logic systems (FLSs), such as interval type-2 FLSs, have been shown to be very well suited to deal with the high levels of uncertainties present in the majority of real-world applications. General type-2 FLSs are expected to further extend this capability. However, the immense computational complexities associated with general type-2 FLSs have, until recently, prevented their application to real-world control problems. This paper aims to address this problem by the introduction of a complete representation framework, which is referred to as zSlices-based general type-2 fuzzy systems. The proposed approach will lead to a significant reduction in both the complexity and the computational requirements for general type-2 FLSs, while it offers the capability to represent complex general type-2 fuzzy sets. As a proof-of-concept application, we have implemented a zSlices-based general type-2 FLS for a two-wheeled mobile robot, which operates in a real-world outdoor environment. We have evaluated the computational performance of the zSlices-based general type-2 FLS, which is suitable for multiprocessor execution. Finally, we have compared the performance of the zSlices-based general type-2 FLS against type-1 and interval type-2 FLSs, and a series of results is presented which is related to the different levels of uncertainty handled by the different types of FLSs.

In this paper we describe the ATRACO (adaptive and trusted ambient ecologies) approach towards next generation ambient intelligent environments. Several agents, such as a fuzzy task agent with learning capabilities and an interaction agent collaborate in a goal-related activity sphere and adapt heterogeneous artifacts within the sphere in order to support the user to fulfill tasks. All components work on a dynamic sphere ontology, which forms the main knowledge base of the ecology. The presented prototype is able to realize the goal ¿feel comfortable at home after work¿ and was implemented in an existing intelligent environment.

Applicant selection and ranking methods for job roles within human resources (HR) systems involve high levels of uncertainty. This is due to the requirement to allow for the varying opinions and preferences of the different occupation domain experts in the decision making process. Hence, there is a need to develop novel systems that will enable HR departments to determine the most important requirements criteria (experience, skills etc) for a given job, based on the preferences of different domain experts, while ensuring that the experts decisions are unbiased and correctly weighted according to their knowledge and experience. This will enable a more effective way to short list submitted candidate CVs from a large number of applicants providing a consistent and fair CV ranking policy, which can be legally justified. This paper presents a novel system using a neuro-fuzzy based agent approach for automatically determining the key skill characteristics defining each expert's preferences and ranking decisions, while handling the uncertainties and inconsistencies in group decisions of a panel of experts. The presented system automates the processes of requirements specification and applicant's ranking. Experiments have been performed within the residential care sector where the proposed system has been shown to produce ranking decisions that were relatively highly consistent with those of the human experts.

Fuzzy logic control is generally credited with being an adequate methodology for real world control applications which are subject to large amounts of uncertainties. Recent work has shown that interval type-2 fuzzy logic controllers (FLCs) can outperform type-1 FLCs in applications which encompass large amounts of uncertainty. However, the application of general type-2 FLCs and investigations of their performance have been very limited. This paper employs the recently introduced concept of zSlices based general type-2 fuzzy sets to implement a zSlices based general type-2 FLC (zFLC). We will present an overview of the implementation and operations of the zFLC for a two-wheel mobile robot navigating in real world outdoor environments. Furthermore, we present a performance analysis of the zFLC which is compared to the type-1 and interval type-2 FLCs.

This paper discusses the requirements of future Home Area Networks (HAN) with respect to resource discovery. We discuss existing methods and provide some experimental data that shows them to be unsuitable for future HANs. In response, we present a new protocol called “Entity Resolution Protocol” (ERP) and experimental data which shows that it significantly outperforms the other methods discussed.

This paper presents an enhanced type-2 fuzzy logic based agent that can be embedded in multi-occupant Ambient Intelligent Environments (AIEs). The agent utilizes the power of type-2 fuzzy logic in modeling various types of uncertainties (especially inter-user uncertainties) and the agent non-intrusively learns the collective behavior of the occupants from their individual type-1 profiles. Knowledge about the users' behavior and preferences is encoded in the form of type-2 rules and membership functions. The agent then uses the captured behavior to control the environment on behalf of the users and adapt in the short term as well as to long term changes in the environment or the users' habits. Multiple real world experiments were carried out in the Intelligent Classroom (iClass) (which is based in the Ambient Intelligence Centre “AMIC” at the German University in Cairo (GUC)), to evaluate the performance of the agent.

In this paper, we will present a new approach for agents embedded in Ambient Intelligent Environments (AIEs). The new approach is based on integrating a speech recognizer with an adaptable intelligent agent for controlling the AIE on the user behalf. The intelligent agent is based on fuzzy systems which is able to learn the user(s) behavior and adapt in a lifelong learning mode to the user changing desires and preferences. The speech recognizer based agent was tested in the German University in Cairo intelligent Classroom (iClass) which is a real world AIE testbed. The results have validated the proposed approach with real experiments involving real users in the iClass.

In this paper, we will present a neural network based approach to extract intro and outré times of songs within an intelligent radio broadcasting studio (iStudio). The iStudio is located in the German University in Cairo (GUC) and it is a testbed for Ambient Intelligent Environments (AIEs) that are related to media and entertainment. The paper targets the challenging problem of producing a system that can predict the music intro and outré times of songs thus avoiding the need to manually measure these times for the huge number of songs that exist in any radio station library. The importance of predicting the music intro and outré times is crucial for realizing Ambient Intelligence (AmI) in radio studios as knowing these times will be essential for controlling the previous and following events of the given songs, especially in talk shows and DJ shows. The paper will explain the employed technique and we will present experiments to justify the success of the employed approach.

In this paper, we describe a novel approach of combining an emotion voice aware system with a fuzzy logic system to develop embedded agents aimed at creating an Ambient Intelligent Environment (AIE). This combined approach was evaluated in the GUC intelligent Classroom (iClass) which a is real world AIE testbed. The developed system aims to control the user environment on his behalf while taking into account the user's emotions. The agent learns online the fuzzy membership functions and rules from the user monitored actions to generate an agent that models the user behavior in an educational AIE like the iClass. The agent then operates in a life long learning mode where the agent can be adapted over long time intervals to the user's changing desires and preferences.

Intelligent congestion control is vital for encoded video streaming of a clip or film, as network traffic volatility and the associated uncertainties require constant adjustment of the bit rate. Existing solutions, including the standard transmission control protocol (TCP) friendly rate control equation-based congestion controller, are prone to fluctuations in their sending rate and may respond only when packet loss has already occurred. This is a major problem, because both fluctuations and packet loss affect the end-user's perception of the delivered video. A type-1 (T1) fuzzy logic congestion controller (FLC) can operate at video display rates and can reduce packet loss and rate fluctuations, despite uncertainties in measurements of delay arising from congestion and network traffic volatility. However, a T1 FLC employing precise T1 fuzzy sets cannot fully cope with the uncertainties associated with such dynamic network environments. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce improved performance. This paper proposes an interval type-2 FLC that achieves a superior delivered video quality compared with existing traditional controllers and a T1 FLC. To show the response in different network scenarios, tests demonstrate the response both in the presence of typical Internet cross-traffic as well as when other video streams occupy a bottleneck on an All-Internet protocol (IP) network. As All-IP networks are intended for multimedia traffic, it is important to develop a form of congestion control that can transfer to them from the mixed traffic environment of the Internet. It was found that the proposed type-2 FLC, although it is specifically designed for Internet conditions, can also successfully react to the network conditions of an All-IP network. When the control inputs were subject to noise, the type-2 FLC resulted in an order of magnitude performance improvement in comparison with the T1 FLC. The type-2 FLC also showed reduced packet loss when compared with the other controllers, again resulting in superior delivered video quality. When judged by established criteria, such as TCP-friendliness and delayed feedback, fuzzy logic congestion control offers a flexible solution to network bottlenecks. These findings offer the type-2 FLC as a way forward for congestion control of video streaming across packet-switched IP networks.

Ranking applicants for a given job is one of the most important processes for Human Resources (HR) systems. The ranking of job applicants involves two main processes which are the specification of the requirements criteria for a given job (experience, skills, etc) and the matching between the applicantspsila profiles and the job requirements. There is currently a strong move towards automating these two processes to generate an applicantspsila ranking system that gives consistent and fair results. However there is a high level of uncertainty involved in these two processes as they involve the input of several experts. These experts will have different opinions, expectations the interpretations for the requirements specification as well as for the applicants matching and ranking. This paper presents a novel approach for ranking job applicants by employing type-2 fuzzy sets for handling the uncertainties in group decisions in a panel of experts. Hence the presented system will enable automating the processes of requirements specification and applicants matching/ranking. We have performed real world experiments in the care domain where our system handled the uncertainties and produced ranking decisions that were consistent with those of the human experts. To the authorspsila knowledge, this will be the first type-2 based commercial software system.

Type-1 fuzzy logic controllers (FLCs) have been widely employed in many control applications as they give a good performance and it is relatively easy to extract the type-1 FLC parameters from experts. However, type-1 FLCs cannot fully handle the encountered uncertainties in changing unstructured environments as they use crisp type-1 fuzzy sets. Consequently, in order for type-1 FLCs to provide a satisfactory performance in face of high levels of uncertainties, some common practices are followed including continuously tuning the type-1 FLC or providing a set of type-1 FLCs where each FLC handles specific operation conditions. Alternatively, type-2 FLCs can handle uncertainties to give a better control performance. However, it is relatively challenging to extract from experts the footprint of uncertainty (FOU) information and consequently the type-2 fuzzy sets for type-2 FLCs. In this paper, we will present a novel method for generating the input and output type-2 fuzzy sets so that their FOUs can capture the faced uncertainties. The proposed method will generate a type-2 FLC that will try to embed the type-1 FLCs corresponding to the various operation conditions faced so far besides embedding a large number of other embedded type-1 FLCs. This will allow the type-2 FLC to handle the uncertainties trough a big number of embedded type-1 FLCs to produce a smooth and robust control performance. We will show through real world experiments how the developed type-2 FLC will handle the uncertainties and give a smooth control response that outperforms the individual and aggregated type-1 FLCs.

Global warming is becoming one of the serious issues facing humanity. Several initiatives have been introduced to deal with global warming including the Kyoto protocol which assigned mandatory targets for the reduction of greenhouse gas emissions to signatory nations. However, over the last decade, commercial buildings worldwide have experienced massive growth in energy costs. This was caused by the expansion in the use of air conditioning and artificial lighting as well as an ever increasing energy demand for computing services. Existing building management systems (BMSs) have, generally, failed to fully optimize energy consumption in commercial buildings. This is because they lack control systems that can react intelligently and automatically to anticipated changes in ambient weather conditions and the many other environmental variables typically associated with large buildings. In this paper, we present a novel agent based system entitled intelligent control of energy (ICE) for energy management in commercial buildings. ICE uses different computational intelligence (CI) techniques (including fuzzy systems, neural networks and genetic algorithms) to dasialearnpsila a buildings thermal response to many variables including the outside weather conditions, internal occupancy requirements and building plant responses. ICE then uses CI based algorithms which work in real-time with the buildingpsilas existing BMS to minimize the buildingpsilas energy demand. We will show how the use of ICE will allow significant energy cost savings, while still maintaining customer-defined comfort levels.

Higher order fuzzy logic systems such as interval type-2 fuzzy logic systems have been shown to be very well suited to dealing with the large amounts of uncertainties present in the majority of real world applications. General type-2 fuzzy logic systems are expected to further extend this capability. However, the complexity as well as the immense computational requirements have generally prevented a foray into general type-2 fuzzy logic research. This paper introduces an alternative approach termed zSlices for representing general type-2 sets based on interval type-2 sets. Thus, this will lead to a smooth transition from interval to general type-2 fuzzy systems. The proposed approach will lead to a significant reduction in both the complexity and the computational requirements for general type-2 fuzzy logic systems. Hence, this will lead to facilitating the application of general type-2 fuzzy logic to many real world applications.

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant associations between services provided by devices and embedded agents residing in ambient intelligent environments (AIEs). The embedded agents perform two processes where the first process monitors the inhabitants of the AIE and learns their behaviors in an online, non-intrusive and life-long fashion. The second process then evaluates the relevance and significance of the associations to various services and eliminates the redundant associations in order to minimize the agent computational latency within the AIE. We will present real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture.

Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 fuzzy logic controllers (FLCs) have shown that they can handle such uncertainties and give a superior performance to the existing commercial controllers. However, there are a number of computational bottlenecks that pose as significant barriers to the widespread deployment of type-2 FLCs in commercial embedded control systems. This paper explores the use of parallel hardware implementations of interval type-2 FLC as a means to eradicate these barriers thus producing bespoke co-processors for a soft core implementation of a FPGA based 32 bit RISC micro-processor. These coprocessors will perform functions such as fuzzification and type reduction and are currently utilised as part of a larger embedded interval type-2 fuzzy engine management system (T2FEMS). Numerous timing comparisons were undertaken between the co-processors and their sequential counterparts where the type-2 co-processors reduced significantly the computational cycles required by the type-2 FLC. This reduction in computational cycles allowed the T2FEMS to produce faster control responses whilst offering a superior control performance to the commercial engine management systems. Thus the proposed co-processors enable us to fully explore the potential of interval and possibly general type-2 FLCs in commercial embedded applications.

The type-2 Fuzzy Logic Controller (FLC) has started to emerge as a promising control mechanism for autonomous mobile robots navigating in real world environments. This is because such robots need control mechanisms such as type-2 FLCs which can handle the large amounts of uncertainties present in real world environments. However, manually designing and tuning the type-2 Membership Functions (MFs) for an interval type-2 FLC to give a good response is a difficult task. This paper will present a Genetic Algorithm (GA) based architecture to evolve the type-2 MFs of interval type-2 FLCs for mobile robots that will navigate in real world environments. The GA based system converges after a small number of iterations to type-2 MFs which give a very good performance. We have performed a series of real world experiments in which the evolved type-2 FLCs controlled a real robot in an outdoor arena. The evolved type-2 FLCs dealt with the uncertainties present in the real world to give a very good performance that has outperformed their type-1 counterparts as well as the manually designed type-2 FLCs.

In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the user's particular behaviors and preferences for controlling the AIE. The agent learns the user's behavior by learning his particular rules and interval type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by genetic algorithms (GAs). We will present unique experiments carried out by different users over the course of the year in the Essex Intelligent Dormitory (iDorm), which is a real AIE test bed. We will show how the type-2 agents learnt and adapted to the occupant's behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases

In this paper, we will present a novel system for learning and incrementally adapting type-2 Fuzzy Logic Controllers (FLCs) for agents embedded in Ambient Intelligent Environments (AIEs). The system learns the rules and the type-2 Membership Functions (MFs) for the type-2 FLC that models the user behavior. Over long term operations, the agent incrementally adapts the type-2 FLC rules and MFs in a life long learning mode to accommodate for the short term and long term uncertainties encountered in AIEs. We will present unique experiments carried out by different users over the course of the year in the Essex intelligent Dormitory (iDorm) which is a real AIE test bed. We will show how the type-2 agent learnt and adapted to the occupant's behavior, whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 fuzzy agents while using smaller rule bases.

Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 Fuzzy Logic Controllers (FLCs) can handle such uncertainties; however they have a computational overhead associated with the iterative type-reduction process which can diminish the FLC real-time performance. Furthermore, manually designing a type-2 FLC is a difficult task particularly as the number of membership function parameters and rules increase. In this paper, we will introduce an embedded Real-Time Type-2 Neuro-Fuzzy Controller (RT2NFC) which overcomes the iterative type-reduction overhead and learns the parameters of interval type-2 FLC for marine engines. We have performed numerous experiments on a real diesel engine testing platform in which we compared our RT2NFC to a T2NFC based on the iterative type reduction procedure. Both T2NFCs were embedded on an industrial microcontroller platform where they handled the uncertainties to produce accurate and robust speed controllers that outperformed the currently used commercial engine controller. The RT2NFC gave approximately the same control response as the T2NFC, whilst the RT2NFC avoided the type-reduction overhead thus giving a faster real-time response.

In this paper, we will introduce a novel system where identical miniaturized robotic agents with limited capabilities will collaborate to form a team that is capable of localizing and repairing scale formations in tanks and pipes within inaccessible fluidic environments. Each robotic agent is an autonomous entity that is based on the biologically inspired spiking neural networks (SNNs) that communicate using pulses or spikes. The weights of the SNN are evolved using adaptive genetic algorithm (GA) that uses adaptive crossover and mutation to converge relatively fast to solutions that allow the robots to complete the desired tasks. The robotic agents communicate using indirect communication to move towards the site of scale formation and collaborate to repair damages.

In this comment, it will be shown that the backpropagation (BP) equations by Wang et al. are not correct. These BP equations were used to tune the parameters of the antecedent type-2 membership functions as well as the consequent part of the interval type-2 fuzzy neural networks (T2FNNs). These incorrect equations would have led to erroneous results, and hence this might affect the comparisons and findings presented by Wang et al. This comment will highlight the correct BP tuning equations for the T2FNN.

Marine propulsion and traction diesel engines operate in highly dynamic and uncertain environments. The current speed controllers for marine/traction diesel engines are based on PID and type-1 fuzzy logic controllers (FLCs) which cannot fully handle the uncertainties associated with such dynamic environments. Type-2 FLCs can handle such uncertainties to produce a better control performance. However, type-2 FLCs have a computational overhead associated with the iterative type-reduction process which can reduce the FLC real-time performance, especially when operating on industrial embedded controllers which have limited computational and memory capabilities. In this paper, we introduce a real-time type-2 FLC that is suited for embedded controllers operating in marine/traction diesel engines. We have conducted numerous experiments where the embedded type-2 FLCs dealt with the uncertainties in real-time and displayed a robust control response that outperformed the PID and type-1 FLCs whilst using smaller rule bases

We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An unsupervised, data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.

This work contains the results of an initial study into the FPGA implementation of a spiking neural network. This work was undertaken as a task in a project that aims to design and develop a new kind of tangible collaborative autonomous agent. The project intends to exploit/investigate methods for engineering emergent collective behaviour in large societies of actual miniature agents that can learn and evolve. Such multi-agent systems could be used to detect and collectively repair faults in a variety of applications where it is difficult for humans to gain access, such as fluidic environments found in critical components of material/industrial systems. The initial achievement of implementation of a spiking neural network on a FPGA hardware platform and results of a robotic wall following task are discussed by comparison with software driven robots and simulations.

We describe a novel system for learning and adapting type-2 fuzzy controllers for intelligent agents that are embedded in ubiquitous computing environments (UCEs). Our type-2 agents operate non intrusively in an online life long learning manner to learn the user behaviour so as to control the UCE on the user's behalf. We have performed unique experiments in which the type-2 intelligent agent has learnt and adapted online to the user's behaviour during a stay of five days in the intelligent dormitory (iDorm) which is a real UCE test bed. We show how our type-2 agent deals with the uncertainty and imprecision present in UCEs to give a very good performance that outperform the type-1 fuzzy agents while using a smaller number of rules.

There are many sources of uncertainty facing the fuzzy logic controller (FLC) for autonomous mobile robots navigating in changing and dynamic unstructured environments. The traditional type-1 FLC using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 fuzzy logic controller (FLC) using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present the type-2 FLC and its novel application for the real time control of mobile robots. We used the type-2 FLC to implement different robotic behaviours on different robotic platforms in indoor and outdoor unstructured and challenging environments. The type-2 FLCs dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1 FLCs whilst using smaller rule bases.

In this paper we introduce a novel mechanism for controlling autonomous mobile robots that is based on using spiking neural networks (SNNs). The SNNs are inspired by biological neurons that communicate using pulses or spikes. As SNNs have shown to be excellent control systems for biological organisms, they have the potential to produce good control systems for autonomous robots. In this paper we present the use and benefits of SNNs for mobile robot control. We also present an adaptive genetic algorithm (GA) to evolve the weights of the SNNs online using real robots. The adaptive GA using adaptive crossover and mutation converge in a small number of generations to solutions that allow the robots to complete the desired tasks. We have performed many experiments using real mobile robots to test the evolved SNNs in which the SNNs provided a good response.

Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.

In this paper we introduce a novel learning and adaptation system for embedded-agents embodied and situated in intelligent inhabited environments. The fuzzy incremental synchronous learning (ISL) techniques we describe seek to provide an online, life-long, non-intrusive method for learning personalised behaviour and anticipatory adaptive control for physical environments.

Addresses the development of a system for online learning of fuzzy behaviour co-ordination for autonomous agents in the form of robots based on genetic algorithms (GAs) and real-time interaction with the environment. The proposed system organises the behaviours hierarchically and uses fuzzy engines to implement both the behaviours and their co-ordination mechanism. In previous work (1999) we reported on our success in the online learning of individual behaviours (rules and membership functions). In this paper we report on a system that allows the fuzzy membership function (MF) for behaviour co-ordination to be learnt online in a manner that satisfies some high level mission or plan. The GAs use adaptive learning parameters and guided constrained optimisation to speed the GAs search and enable it to be performed via real-world interaction rather than off-line simulation. The results of this work are compared with results reported elsewhere and reveals this approach to have a superior learning performance while learning using real outdoor robots in changing environments. The ability to learn co-ordination skills in a short time interval without human intervention makes this approach particularly useful for applications where access is difficult such as nuclear reactors, underwater vehicles and space robots and fast changing and dynamic environments such as the agricultural environments.

We describe a technique which enables a fuzzy-logic based robot control system to automatically determine the membership functions (MF) of the input sensors online and in a short time interval. There is a necessity for such online self-calibration for fast changing and dynamic environments such as agricultural environments and difficult or inaccessible environments, such as nuclear reactors, underwater and space environments. In these media the robot has to learn the appropriate MF with no human intervention taking into account the difference in sensor characteristics in the different environments and changes in production requirements and repairing or otherwise upgrading robots. So there is a necessity to find a fast converging algorithm that can calibrate the MF online in real time with no need for human intervention or simulation. our work reports on an approach based on the use of a modified genetic algorithm to evolve the fuzzy MF of the individual behaviours. The MF of four behaviours were learnt online in an average time of 4 minutes for each behaviour in an outdoor environment. These learnt behaviours were then co-ordinated and tested in complex and dynamic environments in which the robot gave a very good response.

This paper describes the design of a fuzzy controlled autonomous robot, incorporating genetic algorithms (GA) based rule learning, for use in an outdoor agricultural environment for path and edge following activities which involve spraying insecticide, distributing fertilisers, ploughing, harvesting, etc. The robot has to navigate under different ground and weather conditions. This paper addresses the development of an online self-learning system based on modified version of the fuzzy classifier system. The proposed technique has resulted in rapid convergence suitable for learning individual behaviours online without the need for simulation. The controller was tested on both an in-door and out-door mobile robot operating with different types of sensors, propulsion and steering. Experiments include operating the vehicle following irregular crop edges under different weather and ground conditions within a tolerance in the order of 2 inches.