Stroke is a leading cause of long-term disability worldwide. Rehabilitation, though effective, remains a highly resource-intensive process. Integrating assistive robots into cur-rent practices holds significant potential to enhance therapeutic outcomes by addressing workforce limitations and personal-izing care. This stakeholder engagement study explored the perspectives and concerns of physiotherapists and occupational therapists on implementing humanoid robots in stroke reha-bilitation. Drawing insights from this exploratory consultation with stroke rehabilitation experts in the United Kingdom, key directions for future robot development in stroke care were identified. Clinicians expressed enthusiasm about the potential for robots to mitigate workforce shortages, especially for upper limb rehabilitation, and highlighted the importance of robots' ability to analyze patients' motor dynamics, track progress over sessions, and operate autonomously with minimal supervision. Key concerns included the current biomechanical limitations of humanoid robots, doubts about their ability to rehabilitate patients with cognitive impairments, and operational challenges related to setup and use.

Social robots hold great promise for supporting children’s learning, yet their effectiveness may depend on how well they align with individual learner characteristics. This study investigates the role of gender in shaping the outcomes of Learning-by-Teaching (LbT) with a social robot. In a primary school setting, 53 children (aged 8–9) participated in French language tasks under either a robot-assisted LbT condition or a self-practice condition. While no significant effects were found, girls consistently showed higher learning and retention gains when teaching the robot compared to practicing alone, an effect not observed in boys. Contrary to expectations, girls and boys spent similar time on task, used help equally, and reported comparable perceptions of the learning activity. Exploratory analyses revealed that girls who found the task more difficult learnt more, aligning with theories of desirable difficulty, and that higher learning gains were linked to lower perceived competence, suggesting possible signs of Imposter Syndrome. These findings highlight the complex interplay between cognitive and emotional factors in robot-assisted learning and emphasise the need for personalised educational technologies that adapt not only to performance but also to learner identity and psychological experience.

Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns. The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes. One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content.

Recent breakthroughs in event-based vision, driven by the capabilities of high-resolution event cameras, have significantly improved human-robot interactions. Event cameras excel in managing dynamic range and motion blur, seamlessly adapting to various environmental conditions. The research presented in this paper leverages this technology to develop an intuitive robot guidance system capable of interpreting hand gestures for precise robot control. We introduce the "EB-HandGesture" dataset, an innovative high-resolution hand-gesture dataset used in conjunction with our network "ConvRNN" to demonstrate commendable accuracy of 95.7% in the interpretation task, covering six gesture types in different lighting scenarios. To validate our framework, real-life experiments were conducted with the ARI robot, confirming the effectiveness of the trained network during the various interaction processes. This research represents a substantial leap forward in ensuring safer, more reliable and more efficient human-robot collaboration in shared workspaces.

Falls and fall risk management are challenges that are increasing in an aging society, exacerbated by the decreasing availability of care professionals to provide suitable fall management plans. Technology may provide a solution to this, with robotics and vision systems receiving increased attention. A pilot study was conducted using a vision system mounted on a Turtlebot 4, MoveNet, and different machine learning algorithms to assess a Timed Up and Go (TUG) test. The system was evaluated on the performance of a previously trained action classifier and by comparing times for the different phases of the TUG test from the output of the model with the output from the QTUG test acquired by IMU sensors worn by the participants. The results showed the system could determine if the person was sitting, in transition, or standing with high accuracy (97.09%) with higher levels of consistency for participants between tests than the QTUG. This demonstrates that the system is not only advantageous requiring minimal user input but also can match the performance of wearable sensors that are considered the "gold standard" for TUG tests.

Large Language Models (LLMs) are increasingly influential in advancing robotics. This paper introduces ECLAIR (Evaluative Corrective Guidance Language as Reinforcement), a novel framework that leverages LLMs to interpret and incorporate diverse natural language feedback into robotic learning. ECLAIR unifies various forms of human advice into actionable insights within a Reinforcement Learning context, enabling more efficient robot instruction. Experiments with real-world users demonstrate that ECLAIR accelerates the robot’s learning process, aligning its policy closer to optimal from the outset and reducing the need for extensive human intervention. Additionally, ECLAIR effectively integrates multiple types of advice and adapts well to prompt modifications. It also supports multilingual instruction, broadening its applicability and fostering more inclusive human-robot interactions. Project website: https://sites.google.com/view/eclairiros

Abstract Growing evidence shows the potential benefits of robot-assisted therapy for children with Autism Spectrum Disorder (ASD). However, when developing new robotics technologies, it must be considered that this condition often causes increased anxiety in unfamiliar settings. Indeed, children with ASD have difficulties accepting changes like introducing multiple new technological devices in their routines, therefore, embedded solutions should be preferred. Also, in this context, robots should be small as children find the bigger ones scary. This leads to limited computing resources onboard as small batteries power them. This article presents a study on gesture recognition using video recorded only by the camera embedded in a NAO robot, while it was leading a clinical procedure. The video is 2D and low quality because of the limits of the NAO-embedded computing resources. The recognition is made more challenging by robot movements, which alter the vision by moving the camera and sometimes by obstructing it with the robot’s arms for short periods. Despite these challenging real-world conditions, in our experiments, we have tuned and improved state-of-the-art algorithms to yield an accuracy higher than $$90\%$$ 90 % in the gesture classification, with the best accuracy being $$94\%$$ 94 % . This level of accuracy is suitable for evaluating the children’s performance and providing information for the diagnosis and continuous assessment of the therapy. We have also considered the performance improvement of using a low-power GPU-AI accelerator embedded system, which could be included in future robots, to enable gesture analysis during the therapy, which could be adapted to the child’s performance. Graphical abstract

We present a novel, hybrid approach for clustering text databases. We use a genetic algorithm to generate and evolve a set of single word search queries in Apache Lucene format. Clusters are formed as the set of documents matching a search query. The queries are optimized to maximize the number of documents returned and to minimize the overlap between clusters (documents returned by more than one query in a set). Optionally, the number of clusters can be specified in advance, which will normally result in an improvement in performance. Not all documents in a collection are returned by any of the search queries in a set, so once the search query evolution is completed a second stage is performed whereby a KNN algorithm is applied to assign all unassigned documents to their nearest cluster. We describe the method and compare effectiveness with other well-known existing systems on 8 different text datasets. We note that search query format has the qualitative benefits of being interpretable and providing an explanation of cluster construction.

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents' training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans.

Possible applications of robots are growing in educational contexts, where they can support and enhance the traditional learning at any level, including kindergarten. However, the acceptance of such novel technology among the kids is not fully understood, especially for the youngest ones. In this abstract, we present an experiment that investigates the attitude of 52 pre-schooler children before and after the interaction with a humanoid robot in kindergarten setting. The main hypothesis is that ideas and prejudices can change after a controlled interaction with a physical robot. The study found that children exposed to the robot decrease their distress and positively change their attitude toward the technological device. The results suggest that an early, controlled exposure may facilitate future acceptance.

This abstract presents a preliminary evaluation of the usability of a novel system for cognitive testing, which is based on the multimodal interfaces of the social robot “Pepper” and the IBM cloud AI “Watson”. Thirty-six participants experienced the system without assistance and filled the System Usability Scale questionnaire. Results show that the usability of the system is highly reliable.

Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model mathematical cognition in robots. This paper presents an experimental evaluation of the Long- Short Term Memory networks for modeling the simple mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.

Robotic psychological assessment is a novel field of research that explores social robots as psychometric tools for providing quick and reliable screening exams. In this study, we involved elderly participants to compare the prototype of a robotic cognitive test with a traditional paper-and-pencil psychometric tool. Moreover, we explored the influence of personality factors and technology acceptance on the testing. Results demonstrate the validity of the robotic assessment conducted under professional supervision. Additionally, results show the positive influence of Openness to experience on the interaction with robot's interfaces, and that some factors influencing technology acceptance, such as Anxiety, Trust, and Intention to use, correlate with the performance in the psychometric tests. Technical feasibility and user acceptance of the robotic platform are also discussed.

Search queries define a set of documents located in a collection and can be used to rank the documents by assigning each document a score according to their closeness to the query in the multidimensional space of weighted terms. In this paper, we describe a system whereby an island model genetic algorithm (GA) creates individuals which can generate a set of Apache Lucene search queries for the purpose of text document clustering. A cluster is specified by the documents returned by a single query in the set. Each document that is included in only one of the clusters adds to the fitness of the individual and each document that is included in more than one cluster will reduce the fitness. The method can be refined by using the ranking score of each document in the fitness test. The system has a number of advantages; in particular, the final search queries are easily understood and offer a simple explanation of the clusters, meaning that an extra cluster labelling stage is not required. We describe how the GA can be used to build queries and show results for clustering on various data sets and with different query sizes. Results are also compared with clusters built using the widely used k-means algorithm.

Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data.

Attention has been shown to be a predictor of flight performance and, therefore, it is necessary to assess this cognitive ability to evaluate candidate aviation pilots and to verify if the pilot has the sufficient attention level required for the flight duties. In this paper, we present a study that uses a Neuro-fuzzy approach to identify a benchmark model of the aviation pilot attention level. The model is learned from the data examples collected using a computerized battery of seven tests, which was specifically built and validated to assess the main cognitive factors related with the aviation pilot attention in realistic scenarios. Data examples were collected in experimental session with a total of 180 participants, 96 military aviation pilots and 84 untrained people as controls. The aim is to build the model as a classifier that is able to discriminate between the two groups, allowing to identify the peculiar profile of the aviation pilot as opposed to control subjects. Classification performance analysis shows that a hierarchical fuzzy system has a better accuracy than single stage classification algorithms and gives more details about the different attention factors. Moreover, a fuzzy system for our model because it can be readable by human instructors and used as guidance for the training. The ultimate objective of our work is an expert system that will be able to assess the attention performance and compare it against the typical profile of the aviation pilot. This will be used as an instrument for more accurate selection and training of aviation pilots by identifying the areas of deficit that need to be improved, and to measure if a pilot has a sufficient level of attention before the flight.

Research in robotics has made available numerous possibilities and tools for further innovation in the psychological practice. For instance, recent research provided many examples of possible applications of robots in the education and rehabilitation of people with learning difficulties and/or intellectual disabilities. In this paper, we present a study on how cultural backgrounds can influence the perception and intention to use a robot as an instrument in the future practice. The study involved 37 Italian students and 37 UK students, as future professionals in the field of psychology, which experienced the actual capabilities of a humanoid robot through a live demo. In this work, we explored the main factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) with the aim to reveal cultural differences. The instrument used was the UTAUT questionnaire, which was designed and validated to investigate the robot acceptance and use. A significant difference on the intention to use the robot is reported in our results. The discriminant analysis produced a very high degree of separation between the two groups, confirming that there is a different approach toward the use of robotics between the two cultures.

Autism Spectrum Disorder (ASD) is a condition in which deficits in social interaction and social communication can make everyday life difficult. The use of mechanical and electronic devices has proven to be effective in ASD therapy and recently Socially Assistive Robotics (SAR) research has suggested that robots are promising tools for the treatment of this disorder. Starting from these findings, our on-going research aims to identify effective modalities for treatment of ASD through interaction with a robot, and to integrate them into existing therapeutic protocols to improve their efficacy. In this paper we present some preliminary findings of our current work towards this objective. We detail the methodology and give the results of a pilot clinical trial, focused on imitation skills, with three children affected by ASD and Intellectual Disability (ID) under treatment in a research centre specialized in the care of children with disabilities. The success of the experiment suggests that the robot can be effectively integrated in the ASD therapies currently used in the centre. Analysis of these initial results encourages the development of effective protocols in which the robot becomes a mediator between the child with ASD and humans and suggests some research avenues for focus in the future.

Among the artificial intelligence techniques that successfully support computer assisted decision making, fuzzy logic has proved to be a powerful tool in various fields. In particular it is appreciated by clinical practitioners because of their approaches to take a decision require to deal with uncertainties and vagueness in the knowledge and information. One field in which fuzzy sets theory can be applied with great benefit is psychopathology due to the high prominence of sources of uncertainty, that should be taken into account when the diagnosis of intellectual disability must be formulated. Therefore clinical psychologists have often to deal with comorbidities that make the decision process harder because they must evaluate different assessment tools for a correct diagnosis. In our work we investigate the application of computational intelligence methods, and in particular of approaches based on fuzzy logic and its hybridizations, in the psychological assessment by means of theoretical studies and practical experiments with data collected from patients affected by different levels of intellectual disability. In this paper we present a detailed review of the experimental application, with patients under treatment in a clinical centre, of methodologies we propose to generate fuzzy expert systems for the assessment of intellectual disability. Specifically we highlight, with numerical results, how they can be beneficial for the diagnosis and improve efficacy of the administration of psycho-diagnostic instruments and the efficiency of the assessment.

Thanks to recent technological advances and the increasing interest towards the Cognitive Developmental Robotics (CDR) paradigm, many popular platforms for scientific research have been designed in order to resemble the shape of the human body. The motivation behind this strongly humanoid design is the embodied cognition hypothesis, which affirms that all aspects of cognition are shaped by aspects of the body. Thus CDR is based on a synthetic approach that aims to provide new understanding on how human beings develop their higher cognitive functions. Following this paradigm we have developed an artificial model, based on artificial neural networks, to explore finger counting and the association of number words (or tags) to the fingers, as bootstrapping for the representation of numbers in the humanoid robot iCub. In this paper, we detail experiments of our model with the iCub robotic platform. Results of the number learning with propri-oceptive data from the real platform are reported and compared with the ones obtained instead, with the simulated platform. These results support the thesis that learning the number words in sequence, along with finger configurations helps the building of the initial representation of number in the robot. Moreover, the comparison between the real and simulated iCub gives insights on the use of these platforms as a tool for CDR.

In this paper we present the design and technical implementation of a web based Multi-Modal User Interface (MMUI) tailored for elderly users of the robotic services developed by the EU FP7 Large-Scale Integration Project Robot-Era. The project partners are working to significantly enhance the performance and acceptability of technological services for ageing well by delivering a fully realized system based on the cooperation of multiple heterogeneous robots and with the support of an Ambient Assisted Living environment. To this end, elderly users were involved in the definition of the services and in the design of the hardware and software of the robotic platforms from the first stages of the development process and in real experimentation in two test sites. In particular, here we detail the interface software system for multi-modal elderly-robot interaction. The MMUI is designed to run on any device including touch-screen mobiles and tablets that are preferred by the elderly. This is obtained by integrating web based solutions with the Robot-Era middlewares and planner. Finally we present some preliminary results of ongoing experiments to show the successful evaluation of usability by potential users and to discuss the future directions to improve the proposed MMUI software system.

Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach and a cognitive robotics platform, we introduce initial results of an ongoing study that explores the impact linguistic stimuli could have in processes of mental imagery practice and subsequent motor execution and performance. Results are presented to show that the robot used, is able to “imagine” or “mentally” recall and accurately execute movements learned in previous training phases, strictly on the basis of the verbal commands issued. Further tests show that data obtained with “imagination” could be used to simulate “mental training” processes such as those that have been employed with human subjects in sports training, in order to enhance precision in the performance of new tasks, through the association of different verbal commands.

Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.

Today's computer systems have become unbelievably complex. Nowadays register-level design is an overwhelming task, especially in the embedded system area where the time-to-market is very short. Platform based design shifts the challenge on how to tune parametric platforms to achieve the best performance at the smallest cost. This task, called multi-objective design space exploration, requires accurate strategies because the design space is too vast to be exhaustively evaluated. Even using efficient exploration strategies proposed in the literature, simulation times can become a bottleneck in the design flow. In this work we propose a novel approach to application-domain design space exploration using a multi-objective genetic algorithm and employing HPC to reduce exploration times. The genetic algorithm is preceded by a correlation analysis of the different objectives. The search space is thus reduced by combining highly correlated objectives from different domains. We describe the steps needed to parallelize the exploration on the grid, and present the results of extensive testing of the proposed approach. We obtained over one order of magnitude reduction in exploration times without hampering the quality of the solutions. Shorter simulation times allow more ideas to be explored in less time. This leads to shorter product time-to-market and a more thorough design space exploration. Furthermore the combination of correlated objectives favors the design of modern multi-purpose devices.

The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward artificial neural networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.

In the last few years a number of studies have focused on the design of fuzzy rule-based systems which are interpretable (i.e. simple and easy to read), while maintaining quite a high level of accuracy. Therefore, a new tendency in the fuzzy modeling that looks for a good balance between interpretability and accuracy is increasing in importance. In fact, recently multi-objective evolutionary algorithms have been applied to improve the difficult trade-off between interpretability and accuracy. In this paper, we focus both on rule learning and fuzzy memberships tuning proposing a technique based on a multi-objective genetic algorithm (MOGA) to design deep-tuned Fuzzy Rule Based Classifier Systems (FRBCSs) from examples. Our technique generates a FRBCS which includes certain operators (known as linguistic hedges or modifiers) able to improve accuracy without losses in interpretability. In our proposal the MOGA is used to learn the FRBCS and to set the operators in order to optimize both model accuracy and metrics of interpretability, compactness and transparency in a single algorithm. The resulting Multi-Objective Genetic Fuzzy System (MOGFS) is evaluated through comparative examples based on well-known data sets in the pattern classification field.

In this paper we assess the use of high performance computing in design space exploration of a complex highly parameterized very long instruction word based system-on-a-chip platform. Experiments show that the conventional belief of linear decrease in exploration time as the number of available processors increases is discredited starting from a relatively low number of processors mainly due to communication overhead and I/O bottleneck.

Recently, the scientific community has started to show increasing interest in finding clusters in high-dimensional data sets such as gene product (protein or RNA) data sets in bio-informatics. In this paper we consider the problem of finding fuzzy clusters in such very high dimensional data. In fact, even if fuzzy clustering has been successfully applied to numerous data sets, for such high-dimensional databases it often produces trivial solutions where all cluster centers coincide and all memberships are equal. To solve this problem, we present an evolutionary approach that integrates fuzzy c-means clustering and feature selection. Reducing the dimensionality of the space, feature selection improves the quality of the partitions generated, and, at the same time, can help to build both faster and more cost-effective predictors, as well as a better understanding of the underlying generation process. We exhibit the good quality of the clustering results by applying our approach to two real-world data sets from bio-informatics.

This paper deals with the application of unsupervised fuzzy clustering to high dimensional data. Two problems are addressed: groups (clusters) number discovery and feature selection without performance losses. In particular we analyze the potential of a genetic fuzzy system, that is the integration of a multi-objective evolutionary algorithm with a fuzzy clustering algorithm. The main characteristic of the integrated approach is the ability to handle the two problems at the same time, suggesting a Pareto set of trade-off solutions which could have a better chance of matching the real needs. We exhibit the high quality clustering and features selection results by applying our approach to a real-world data set.

The design of a complex embedded system is dominated by the definition of an optimal architecture in relation to certain performance indexes. This activity, known as Design Space Exploration (DSE), is a great challenge for the EDA (Electronic Design Automation) community. The enormous size of the design space, in fact, together with the long simulation time required to evaluate each system configuration during the exploration process, cause DSE to become a bottleneck in the design flow. In this paper we propose a Multiobjective Design Space Exploration methodology based on a Genetic Fuzzy System, the aim of which is to drastically reduce the exploration time while guaranteeing a high level of accuracy. The methodology uses a Genetic Algorithm (GA) for heuristic exploration and a Fuzzy System to evaluate the configurations visited. Although of general application, the methodology is applied to a real case study: optimization of the performance and power consumption of an embedded architecture based on a Very Long Instruction Word (VLIW) microprocessor in a mobile multimedia application domain. The results obtained are compared, in terms of both accuracy and efficiency, with the state of the art in multiobjective DSE strategies, represented by the classical GA approach, demonstrating the scalability and effectiveness of the proposed approach.

In the last few years a number of studies have proposed algorithms that can obtain fuzzy systems which are simple and easy to read, while maintaining quite a high level of accuracy. Following this philosophy, the paper presents a simple, new approach based on Genetic Algorithms, with the aim of selecting the features and tuning the parameters of a fuzzy classification algorithm. From the results obtained by the optimized classifier a transparent, efficient fuzzy system is generated using simple heuristic methods. The main features of the approach are accuracy, scalability, adaptability and expandability. Comparative examples based on three data sets well known in the pattern classification field are given, showing that the approach leads to classifiers with a small number of transparent, readable rules, which are less complex than those reported in the literature with comparable or better accuracy.

In medical diagnosis, a correct disease classification is needed to choose the right treatment and to assure a quality of life that is suitable for a patient's condition. In order to meet this need we researched a technique that allows us to perform automatic diagnoses efficiently and reliably and at the same time is easy for practitioners to use. In this paper we present an efficient computational intelligence technique that integrates fuzzy logic and genetic algorithms in order to discover a transparent fuzzy rule based diagnostic system from data. To improve precision without losses in readability we propose the use of linguistic hedges. The approach has been applied to three real-world benchmarks and compared with related works, showing its effectiveness.

In the current Internet scenario increasing interest is being shown in quality of service (QoS) issues, so simulation and parameter tuning are becoming very hot topics. In this paper we present a user-friendly, easily expandable and time-saving tool, which is able to perform network simulation through fluid-flow models and parameter tuning with parallel genetic algorithms. All the network elements involved (TCP sources, links, routers and so on) are modeled with the fluid-flow approach as independent systems: their outputs only depend on their inputs and internal state variables. Network elements can be connected to each other by a powerful graphical interface. Subsequently the user can select network parameters to be optimized in order to achieve prearranged QoS guarantees. During this phase the tool adopts a parallel implementation of a genetic algorithm (GA) which reduces the execution time required for optimization. A case study is also presented.