In this work, we propose a novel method for Bayesian Networks (BNs) structure elicitation that is based on the initialization of several LLMs with different experiences, independently querying them to create a structure of the BN, and further obtaining the final structure by majority voting. We compare the method with one alternative method on various widely and not widely known BNs of different sizes and study the scalability of both methods on them. We also propose an approach to check the contamination of BNs in LLM, which shows that some widely known BNs are inapplicable for testing the LLM usage for BNs structure elicitation. We also show that some BNs may be inapplicable for such experiments because their node names are indistinguishable. The experiments on the other BNs show that our method performs better than the existing method with one of the three studied LLMs; however, the performance of both methods significantly decreases with the increase in BN size.

Abstract Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.

Humans often use expressions with vague terms which play a fundamental role for effective communication. These expressions are successfully modeled with fuzzy technology, but they are not usually integrated yet with Natural Language Processing models and techniques. Large-scale pre-trained language models yield excellent results in many language tasks, but they have some drawbacks such as their lack of transparency and thorough temporal reasoning capabilities. Therefore, the use of such models may provoke inconsistent or incorrect dialogues in the context of conversational agents which were aimed at providing users of intelligent systems with interactive explanations. In this paper, we propose a model for fuzzy temporal reasoning to overcome some inconsistencies detected in pre-trained language models in a specific application domain of a conversational agent carefully designed for providing users with explanations which are endowed with a good balance between naturalness and fidelity. More precisely, starting from a knowledge graph that provides an intuitive representation of the entities and relations in the application domain, we describe how to map the temporal information onto a fuzzy temporal constraint network. This formalism allows to represent imprecise temporal information and provides mechanisms for checking consistency in conversations. In addition, as a proof of concept, we have developed TimeVersa, a conversational agent which integrates the proposed model into an application domain (i.e., a virtual assistant for tourists) that requires handling imprecise temporal constraints. We illustrate in a use case how the agent can identify temporal inconsistencies and answer queries related to temporal information properly. Results after a user study report that users' perception of consistency is significantly higher in a conversation with TimeVersa than in a similar conversation using the well-known GPT-3 Large Language Model, when vague temporal information is involved. The proposed approach is a step forward for developing conversational agents operating in application domains that require temporal reasoning under uncertainty.

The aim of this paper is to propose a new approach for the automatic treatment of linguistic vagueness. Our motivation is the feeling that most existing approaches dealing with linguistic information are based on converting vague meaning into crisp meaning using some conversions to precise measurements. As a result, existing approaches are adequate and easy to implement, but do not closely model the human thought process. To help alleviate this deficiency, we propose the use of linguistic relations to provide a natural language interface to an end user. We show a possible linguistic Prolog model based on an extension of the syntactic unification algorithm using synonymy and antonymy, as well as the extension of the resolution principle. Our approach does not aim to provide a well-founded formal semantics for such a linguistic Prolog, but a simple model supported by two experiments focused on the use of vague language, both of them executed in Spanish (an analysis of the data of the first experiment it is also available in that language at [1]). Thus, the purpose of this paper is to contribute to the mechanization of approximate reasoning by being respectful of the semantics of the vague terms involved in it; i.e., by paying attention to how they are evaluated by linguistic users under experimentation.

In this paper we experimentally assess, from both algorithmic and pragmatic perspectives, the adequacy of linguistic descriptions of real data generated by two metaheuristics: simulated annealing and genetic algorithm meta-heuristics. The type of descriptions we consider are fuzzy quantified statements (both Zadeh's type-1 and type-2) involving three well-known quantification models (Zadeh's scalar and fuzzy and Delgado's GD). We conducted an empirical validation using real observation and prediction meteorological data, where both automatic (metrics-based) and manual (human experts-based) assessment on the adequacy of the generated descriptions was assessed. Results indicate that, overall, the genetic approach performs better than simulated annealing in terms of quality of the obtained descriptions and time execution. Significance of this outperforming depends on the type of meteorological data and the quantification model selected. Tests of statistical significance point out that for type-1 descriptions no significant differences exist between the two meta-heuristics in the prediction case. For type-2 descriptions, significant differences exist for Delgado's GD model for both types of data. For Zadeh's scalar and fuzzy quantification significance depends on the type of data (observation or prediction). Globally, outperforming of the genetic approach over simulated annealing i) is significant in 4 out of 12 scenarios considered (all of them type-2), and ii) is not significant in the other 8 out 12 scenarios (all type-1 and two type-2). Also human expert assessment on the adequacy of the descriptions was conducted, showing that both meta-heuristics behave similarly for type-1 descriptions, while genetic algorithms produce more suitable type-2 linguistic descriptions.

In this paper, we propose a framework for the automatic generation of natural language descriptions of healthcare processes using quantitative and qualitative data and medical expert knowledge. Inspired by the demand of novel ways of conveying process mining analysis results of healthcare processes (Rojas et al., 2016), our framework is based on the most widely used Data-To-Text (D2T) pipeline (Reiter, 2007) and on the usage of process mining techniques. Backed by a general model that handles process data, this framework is able to quantify attributes in time during a process life-span, recall temporal relations and waiting times between events and its possible causes and compare case (patient) attributes between groups, among other features. Through integrating fuzzy quantification techniques, our framework is able to represent relevant quantitative process information with some degree of uncertainty present on it and describe it in natural language involving uncertain terms. A real application over the Aortic Stenosis Integrated Care Process of the University Hospital of Santiago de Compostela is presented, showcasing the potential of our framework for providing natural language descriptions of healthcare processes addressed to medical experts. Following the standards of D2T systems, manual human validation was conducted for the generated natural language descriptions by fifteen medical experts in Cardiology. Validation results are very positive, since a global average of 4.07/5.00 was achieved for questions related to understandability, usefulness and impact of the natural language descriptions on the medical experts work. More precisely, results indicate i) that the modality which conveyed the information most efficiently was natural language ii) a very clear preference of texts over the usual graphic representation of process information as the way for conveying information to experts (4.28/5.00), and iii) natural language descriptions provide relevant and useful information about the process, allowing for its improvement.

Abstract In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customers. Activity patterns are estimated with process mining techniques and the key information of localization. Localization is obtained with a parallelized solution based on WiFi fingerprint system to speed up the solution. In agreement with the best practices for human evaluation of natural language generation systems, the linguistic quality of the generated report was evaluated by 41 experts who filled in an online questionnaire. Results are encouraging, since the average global score of the linguistic quality dimension is 6.17 (0.76 of standard deviation) in a 7-point Likert scale. This expresses a high degree of satisfaction of the generated reports and validates the adequacy of automatic natural language textual reports as a complementary tool to process model visualization.

In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relations and temporal distances between events, among other features. Through integrating process mining techniques and fuzzy sets within the usual Data-to-Text architecture, our framework is able to extract relevant quantitative temporal as well as structural information from a process and describe it in natural language involving uncertain terms. A real use-case in the cardiology domain is presented, showing the potential of our model for providing natural language explanations addressed to domain experts.

In this paper we evaluate empirically whether there exist significant differences in the numerical results produced by six well-known fuzzy quantification models when applied to the evaluation of unary and binary fuzzy quantified statements on numerical data sets. The models we analyzed are: Zadeh's scalar and fuzzy cardinality, Yager's OWA, Delgado's GD, Sugeno integral and Vila's VQ. These models were tested by evaluating the degree of fulfillment they produced on fifteen numerical data sets from the UCI Machine Learning repository for all the possible fuzzy quantified statements generated by partitions of up to seven quantifiers and linguistic terms of the variables involved. We conducted tests of statistical significance for these evaluation results under a pair-wise comparison. Results indicate that no significant differences were found among the models for unary quantifiers involving a single imprecise property, with a single exception of very limited outreach. For binary quantified statements involving two imprecise properties, significant differences were observed in general among all the pairs of fuzzy quantification models under study. Therefore, in spite of unary models fulfill different theoretical properties, the models under study exhibit very similar empirical behaviour. For binary models, results point out that the selection of a particular model should be guided by other criteria (e.g. the properties they fulfill) different than their experimental behaviour, which is empirically proved to be different.

Artificial Intelligence (AI) is a first class citizen in the cities of the 21st century. In addition, trust, fairness, accountability, transparency and ethical issues are considered as hot topics regarding AI-based systems under the umbrella of Explainable AI (XAI). In this paper we have conducted an experimental study with 15 datasets to validate the feasibility of using a pool of gray-box classifiers (i.e., decision trees and fuzzy rule-based classifiers) to automatically explain a black-box classifier (i.e., Random Forest). Reported results validate our approach. They confirm the complementarity and diversity among the gray-box classifiers under study, which are able to provide users with plausible multi-modal explanations of the considered black-box classifier for all given datasets.

In this work we present an experimental comparison of six widely used quantification methods (Zadeh's scalar and fuzzy cardinality, Yager's OWA, Delgado's GD, Sugeno integral and Vila's VQ) when evaluating Type-1 and Type-2 linguistic descriptions of data generated from meteorological data provided by the Galician Meteorological Agency MeteoGalicia. The objective of this study is to evaluate if there are significant differences among these models for the data considered. We ranked the generated descriptions based on their degree of truth for each quantification model and we analyzed those results calculating the Pearson correlation coefficient. Results show that there are not significant differences in the models when evaluating Type-1 descriptions. However, in Type-2 evaluation the methods can be grouped in three clusters with a significantly different behavior among them: i) Zadeh's scalar cardinality, Delgado's GD and Zadeh's fuzzy cardinality, ii) Yager's method and iii) Vila's VQ.

ExpliClas is a web service aimed at providing users with multimodal (textual + graphical) explanations related to Weka classifiers. In ExpliClas, two types of explanations are automatically generated. On the one hand, global explanations pay attention to the behavior of the classifier as a whole, i.e., they refer to a list of structural properties (number of classes, features, etc.) along with quality indicators such as accuracy or confusion matrix. On the other hand, local explanations go in depth with how the classifier deals with single instances. Current version of ExpliClas already explains classifications made by three different decision tree Weka implementations (J48, RepTree, and RandomTree) and one fuzzy algorithm (FURIA). In this paper, we describe ExpliClas in detail and illustrate its use with the Bank telemarketing dataset.

In this work we present a state lattice based approach for motion planning in mobile robotics. Sensing and motion uncertainty are managed at planning time to obtain safe and optimal paths. To do this reliably, our approach estimates the probability of collision taking into account the robot shape and the uncertainty in heading. We also introduce a novel graduated fidelity approach and a multi-resolution heuristic which adapt to the obstacles in the map, improving the planning efficiency while maintaining its performance. Results for different environments, shapes and motion models are reported, including experiments with real robots.

In this paper, we describe SimpleNLG-GL, an adaptation of the linguistic realisation SimpleNLG library for the Galician language.This implementation is derived from SimpleNLG-ES, the English-Spanish version of this library.It has been tested using a battery of examples which covers the most common rules for Galician.

In this paper, we describe VICLEO, a visualization tool that performs a graph representation of the meaning of a statement based on the concepts of semantic field and semantic similarity measures. As the main novelty, VICLEO extends the concept of semantic field to a whole sentence and clusters it into fuzzy sets, where the degree of membership of the sentence components is obtained applying semantic similarity measures. Furthermore, VICLEO is endowed with a visual interactive interface which allows the user to navigate through the semantic field of the statement.

The reduction of energy consumption in buildings is one of the goals to improve energy efficiency. One way to achieve energy savings in buildings is to develop intelligent control heating strategies that are able to reduce the power consumption by predicting the behavior of the thermal dynamics under different control schemes. One way to accomplish this is by means of learning fuzzy rules using the data collected from different sensors installed in buildings to generate regression models that are accurate and interpretable, so the generated models can be understood by the experts who approve the energy-saving schemes. However, one important issue is the generation of accurate knowledge bases of fuzzy rules for regression that can scale with the large amount of information generated by the many sensors installed in buildings, which will continue to grow in the coming years. For this purpose, in this paper we evaluate the scalability of two genetic fuzzy systems, FRULER and S-FRULER in the domain of thermal dynamics in buildings, using real data from a residential college at the USC.

We describe SimpleNLG-ES, an adaptation of the SimpleNLG realization library for the Spanish language.Our implementation is based on the bilingual English-French SimpleNLG-EnFr adaptation.The library has been tested using a battery of examples that ensure that the most common syntax, morphology and orthography rules for Spanish are met.The library is currently being used in three different projects for the development of data-to-text systems in the meteorological, statistical data information, and business intelligence application domains.

The SoftLearn Activity Reporter is a data-to-text service, which automatically generates textual reports about the activity developed by students within the SoftLearn virtual learning environment. In this paper, we describe the conception of the service, its architecture, and its subsequent evaluation by an expert pedagogue, where 20 full reports generated from real data from an undergraduate course supported by the SoftLearn platform were assessed. Results show that the automatically generated reports are a valuable complementary tool for explaining teachers and students the information comprised in a learning analytics dashboard.

In genetic fuzzy systems (GFS) the size of the problem has a huge influence in the performance of the obtained models, since i) the fuzzy rule bases learned suffer from exponential rule explosion when the number of variables increases, and ii) the convergence time increments with the number of examples. In this paper we present S-FRULER, a scalable distributed version of FRULER which is a GFS that learns simple and linguistic TSK-1 knowledge bases for regression problems. S-FRULER obtains models with high accuracy and low complexity, whilst reducing the algorithm runtime. S-FRULER focuses on splitting the problem into smaller partitions and incorporates a feature selection process for reducing the number of variables used in each partition. Each partition is then solved independently using the FRULER algorithm. The Reduce function obtains linguistic TSK fuzzy rule bases from the information generated in each partition. S-FRULER has been validated in terms of scalability, precision and complexity using 10 large-scale datasets and has been compared with three state of the art GFSs. Experimental results show that S-FRULER scales well while achieving simple models with a linguistic approach and a precision comparable with approximative models.

Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the approaches considered focused on time series data, that are one of the most frequent datasets in real application domains, they can be used for quality assessment of linguistic descriptions generated for any type of data.

In this paper we present a general model for building linguistic descriptions of data (LDD) solutions, which is based on computational models of perception inspired in the computational theory of perceptions (CTP) and in fields of knowledge different from the computational intelligence area. The elements in the model aim to consider the richness and complexity that real LDD processes are endowed with and their actual role in data-to-text natural language generation (D2T-NLG) systems. As an example of application, the building of a LDD solution in the meteorology realm is presented.

One of the most suited techniques for comparing results obtained from computational intelligence algorithms is the statistical hypothesis testing. This method can be used to contrast if the difference between the algorithm with the best results and other algorithms is actually significant. In this paper, we present STAC (Statistical Tests for Algorithms Comparison), a new platform for statistical analysis to verify the results obtained from computational intelligence algorithms. STAC consists of three different layers for performing statistical tests: a Python library, a set of web services and a web client. We show several use cases, in which both non-expert and expert users interact with the web client and use the web services in different programming languages.

In many real problems the regression models have to be accurate but, also, interpretable in order to provide qualitative understanding of the system. In this realm, the use of fuzzy rule base systems, particularly TSK, is widely extended. TSK rules combine the interpretability and expressiveness of rules with the ability of fuzzy logic for representing uncertainty, and the precision of the polynomials in the consequents. In this paper we present a new genetic fuzzy system to automatically learn accurate and simple linguistic TSK fuzzy rule bases that accurately model regression problems. In order to reduce the complexity of the learned models while keeping a high accuracy, we propose a Genetic Fuzzy System which consists of three stages: instance selection, multi-granularity fuzzy discretization of the input variables, and the evolutionary learning of the rule base using Elastic Net regularization. This proposal was validated using 28 real-world datasets and compared with three state of the art genetic fuzzy systems. Results show that our approach obtains the simplest models while achieving a similar accuracy to the best approximative models.

In this paper we present the Soft Learn Activity Reporter (SLAR) service which automatically generates textual short-term reports about learners' behavior in virtual learning environments. Through this approach, we show how textual reporting is a coherent way of providing information that can complement (and even enhance) visual statistics and help teachers to understand in a comprehensible manner the behavior of their students during the course. This solution extracts relevant information from the students' activity and encodes it into intermediate descriptions using linguistic variables and temporal references, which are subsequently translated into texts in natural language. The examples of application on real data from an undergraduate course supported by the Soft Learn platform show that automatic textual reporting is a valuable complementary tool for explaining teachers and learners the information comprised in a Learning Analytics Dashboard.

We present in this paper an application that automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data, together with a natural language generation (NLG) system. The application, which is named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the NLG system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology, which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation, GALiWeather will be released as a real service, offering custom forecasts for a wide public.

Syllogistic reasoning is a type of inference pattern that involves quantified statements through the chaining of two terms by a middle term. In its classical version, only classical crisp quantifiers (all, no, some and some...not) and crisp sets in the terms are considered. If the syllogism involves vagueness, this can appear in the quantifiers (fuzzy quantifiers) or in the terms. The case of the vagueness in the quantification is analysed in Fuzzy Logic under the label of fuzzy syllogism but vagueness in the terms was not approached before now in the same depth. In this paper, we study this kind of syllogism and make a proposal for qualifying the conclusion by a degree of confidence that depends on the degree of similarity between the middle terms.

The tradeoff between bias and variance is a well-known problem in machine learning, since algorithms are expected to achieve a reduced training error without going into overfitting. In Genetic Fuzzy Systems (GFSs), overfitting is usually avoided through the control of the number of rules and/or the number of labels. However, in many machine learning approaches, variance is reduced through the use of a validation set. Inspired by this idea, we propose in this paper an Instance Selection (IS) algorithm for regression problems called Class Conditional Instance Selection for Regression (CCISR) which is based on CCIS [1]. The output of CCISR is used in a GFS to obtain Rule Bases with a low variance, as the rules are generated with an ad hoc data driven method guided by the selected instances, but the error is still measured with the full training dataset. The combined system has been tested over 12 publicly available datasets, and results were compared with other GFSs. Our approach is capable of achieving a reduction in the number of rules while maintaining a good accuracy.

In this paper an approach for taking advantage of the information available in the social network tools of Learning Management Systems is presented. Specifically, our objective is to determine domain experts from their participation in the social networks. Our approach first integrates the different sources of information through an ontology based on the Dublin Core Metadata, and then applies Semantic Web technology to filter and classify each one of the social resources, so providing the basis for a better precision in the information retrieval process. The solution has been implemented following the paradigm of service oriented architecture.

Learning objects have arisen in response to the need of high-quality and reusable instructional materials. The repositories that hold learning objects allow educators to create and share their instructional contents in an organized infrastructure and where information should be easily searchable. Therefore, a key point of these repositories is the way learning objects are categorized. In this paper, we present an approach for classifying semantically learning objects whose metadata are described in Dublin Core. Specifically, our objective is to annotate automatically the subject of the learning object with instances of the DBpedia ontology, that is, to annotate the learning objects repository with linked open data. The syntactic and semantic analysis of the learning object will drive the selection of the most appropriate categories in the linked data repository.

Nowadays a greater number of domains require an improvement in the flexibility of its e-learning systems to so facilitate the adaptation of their course to their user needs. Some adaptive learning systems have been developed in recent years to cover part of these needs, and provide the ability to redirect the student learning flow based on the knowledge base (mainly rules) defined by the teacher. However, these systems, most of them based on the IMS Learning Design specification, have a major restriction: once the course has been published, it can not be changed, regardless that the circumstances in which the course is taught have changed. To remedy this lack, in this paper we present a solution to adapt the structure of units of learning at runtime. This solution extends a proposal for modelling IMS Learning Design through workflows with new capabilities that facilitate the replacement of plays, events and activities on the fly.

In this paper a discussion on some of the key problems in fuzzy linguistic summarization of data in introduced. Also a description on the stages that the summarization process involving fuzzy quantified statements should accomplish is presented.

In this paper we present an approach for improving a specific class of semantic annotation, that relates a term of the document with a (sub)tree of the ontology, instead of linking a term with a single concept of the ontology. An important part of this class of annotation is filtering the relevant (sub)nodes and relations, because the returned graph should only contain relevant information, that is, nodes that are truly related with the topics of the document. In addition, we consider that the relevance of nodes vary depending on if the node is a branch or a leaf, that is, if the node has links to other nodes or it is a text-based description. This paper focuses on the relevance of branch nodes, which is calculated from the relevance of its links, since leaf nodes relevance is usually estimated by similarity metrics. Specifically, our approach incises in learning (through a genetic algorithm) and assigning the most appropriate weights to these links in order to reduce the precision/recall curve of the annotation process. The results show that our solution is viable and outperforms the state of the art approaches.

In this paper, the Aristotelian approach is proposed as an adequate framework for analyzing, categorizing and reviewing the fuzzy models for syllogistic reasoning proposed in the literature. From the analysis performed it is concluded that Zadeh and Yager's models present an incompatible structure with Aristotelian syllogistics although they can be considered as an alternative approach to syllogistic reasoning. Dubois and Prade's, nevertheless, develop an approach that is compatible with the Aristotelian framework. Although none of the revised proposals are genuinely fuzzy, the latter can be considered as an starting point to build an extension of the Aristotelian system to the fuzzy field.

This paper describes a service-oriented architecture that facilitates the execution of learning units, based on the IMS Learning Design specification (IMS LD), in a metaverse platform like Second Life or OpenSim. This architecture ex-ternalizes the functionalities of the IMS LD engine through web services that are invoked by metaverse platform scripts. A software module that translates the requests of the virtual platform has been developed in order to facilitate this invocation and a virtual teaching scenario has been designed to test the feasibility of this architecture.

In this paper an ontology-based Petri net engine to execute units of learning based on the IMS Learning Design (IMS LD) specification is presented. This engine has a layered architecture that enables the execution of IMS LD learning flows by means of high-level Petri nets representing the semantics of the IMS LD dynamic elements. Even if changes in the specification were made, the engine would not have to be re-implemented, and only the introduction of new rules and/or axioms in the ontology would required. Moreover, a service-oriented architecture has been implemented for allowing web-based learning management systems to access to the IMS LD engine.

Fuzzy quantification supplies powerful tools for handling linguistic expressions. Nevertheless, its advantages are usually shown at the theoretical level without a proper empirical validation. In this work, we review the application of fuzzy quantification in two application domains. We provide empirical evidence on the adequacy of fuzzy quantification to support different tasks in the context of mobile robotics and information retrieval. This practical perspective aims at exemplifying the actual benefits that real application can get from fuzzy quantifiers. © 2009 Wiley Periodicals, Inc.

Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron networks.

Detection of people and other moving objects is fundamental for the development of tasks by an autonomous mobile robot, and principally for human-robot interaction. In this paper we present an evolutionary algorithm to learn a pattern classifier system based on the quantified fuzzy temporal rules (QFTRs) model, for the detection of moving objects using laser range finders data. QFTRs are able to analyze the persistence of the fulfillment of a condition in a temporal reference by using fuzzy quantifiers. Experimental results with a Pioneer II robot in a typical hallway environment show an excellent classification rate in a real and complex situation with people moving in several groups in the surrounding.

In this paper, we propose a new quantifier fuzzification mechanism which is deeply rooted in the theory of probability. This quantifier fuzzification mechanism skips the nested assumption, which is inherent to other probabilistic quantification methods. The new quantification approach complies with the properties required for determiner fuzzification schemes (DFS) with finite sets and, hence, its good behavior is assured. Moreover, this new approach is suitable for some application domains. In particular, the use of fuzzy quantifiers for implementing query quantified statements for information retrieval exemplifies the adequacy of the new proposal. The new quantifier fuzzification mechanism has been efficiently implemented and empirically tested for a retrieval task. This practical evaluation followed the standard methodology in the field of information retrieval and was conducted against a popular benchmark consisting of a large collection of documents. The retrieval performance evaluation made evident that: 1) the new method can work in realistic scenarios, and 2) it can overcome recent proposals for applying fuzzy quantifiers in information retrieval

Detection of landmarks is essential in mobile robotics for navigation tasks like building topological maps or robot localization. Doors are one of the most common landmarks since they show the topological structure of indoor environments. In this paper, the novel paradigm of fuzzy temporal rules is used for detecting doors from the information of ultrasound sensors. This paradigm can be used both to model the necessary knowledge for detection and to consider the temporal variation of several sensor signals. Experimental results using a Nomad 200 mobile robot in a real environment produce 91% of doors were correctly detected, which show the reliability and robustness of the system.

This paper describes an Integral Technical Information Management System (ITIMS), which has been designed to operate in industrial fields where it is necessary to monitor a number of processes. ITIMS is a distributed instrumentation environment with ubiquitous access to information that has been developed using standard information and communications technologies, and which is currently operative in the ENDESA-As Pontes coal-fired power station, located in Galicia (NW Spain).

This work describes the design and implementation of a knowledge based system that automatizes the furniture budgeting task. The aim of this task is to estimate the production cost that would suppose a furniture order manufacturing. Fulfillment of this aim requires the management of the company resources, information and knowledge in order to determine workload and financial impacts of client request manufacturing. Task modelling has been done through the workflows formalism approach and describes working activities and its relationships with the company resources. The system software architecture is based on workflow modeling and is an adjustment of a conceptual architecture proposed to be a standard for workflows systems.

A framework for description of fuzzy quantification models is presented. Within this framework, the fuzzy quantified statements evaluation problem is described as the compatibility between the fuzzy quantifier and a fuzzy cardinality or a fuzzy aggregation measure. A list of desirable properties for quantification models is presented and those models that fit the framework are confronted with it.

Two approaches are presented for the modelling of fuzzy operators on fuzzy domains. The approaches are based on crisp definitions that operate onto crisp representatives of the involved fuzzy sets, which are obtained by using bivalued and trivalued level-cuts. Application examples in the field of fuzzy temporal rules-based reasoning are presented.

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained.

In this paper, an enhanced model of fuzzy temporal knowledge bases (FTKBs) formed by fuzzy temporal rules is introduced, and its projection onto the formalism of fuzzy Petri nets (FPNs) is presented. The main objective is to enhance the expressiveness of the rules making up the FTKB, regarding a previous proposal,at the same time as keeping the computation cost within the limits that allow us to work on-line in dynamic environments. This is attained by means of a partial compaction of the rules, prior to their on-line execution. The projection of the FTKB over an FPN makes its design, analysis, and execution easier, by explicitly bringing out all its variables and relationships. © 1999 John Wiley & Sons, Inc.

In this paper, we consider the problem of executing a fuzzy knowledge base (FKB) with rule chaining. The inference process used as starting point is the one based on forward reasoning functions which, obtained from the compositional rule of inference, permits performing the execution of rules in the truth space. This way the process is totally independent from the universes of discourse in which the different variables are defined, allowing a homogeneous treatment for all the variables in the FKB. The execution of the rules is interpreted as the "propagation" of linguistic truth values of the linguistic truth variable that reflect the linguistic degree of fulfillment of each of the propositions in the rules. This execution process is analyzed in two fields of application: control systems, where it is customary to assume t-norm operators as implication functions, and the aggregation process is implemented through the maximum operator and expert systems applications, where other implication functions may be needed and t-norm operators are generally used as aggregation operators. For both of these situations, we present a compaction mechanism which allows a noticeable part of the operations to be performed a priori, thus achieving an important computation time saving.

We develop a representational model for the knowledge base (KB) of fuzzy production systems with rule chaining based on the Petri net formalism. The model presents the execution of a KB following a data driven strategy based on the sup-min compositional rule of inference. In this connection, algorithms characterizing different situations have been described, including the case where the KB is characterized by complete information about all the input variables and the case where it is characterized by ignorance of some of these variables. For this last situation we develop a process of "incremental reasoning"; this process allows the KB to take information about previously unknown values into consideration as soon as such information becomes available. Furthermore, as compared to other solutions, the rule chaining mechanism we introduce is more flexible, and the description of the rules more generic. The computational complexity of these algorithms is O((C/2+M+N)R/sup 2/) for the "complete information" case and O((M+N)R/sup 2/) and O(2(M+N)R/sup 2/) for the other cases, where R is the number of fuzzy conditional statements of the KB, M and N the maximum number of antecedents and consequents in the rules and C the number of chaining transitions in the KB representation.>