Fuzzy Sets and Knowledge Representation

To enable artificial systems to meaningfully use a semantic language of communication is one of the long-term and key targets not only in the field of artificial cognitive agents, but also of AI research in general. Given existing solutions for grounding of modal statements of a language of communication and an idea to model internal concepts of the agent as zadehian fuzzy-linguistic concepts, this paper shows how to meaningfully combine the two within a single framework. An accomplished goal is a model for grounding of modal and non-modal statements of a language of communication based on concepts modelled internally as fuzzy sets spanned over the domain of observation. This paper describes a way in which fuzzy-linguistic concepts are activated by perceptual inputs and how an agents grounds respective non-modal statements. Further, an agent supposed to describe an unobserved part of the environment can use autoepistemic operators of possibility, belief, and knowledge to describe its cognitive attitude toward it. It is discussed how the modal extensions of statements with fuzzy-linguistic concepts should be grounded in order to preserve the common-sense. The resulting constraints put on the model of grounding are formally represented in a form of analytical restrictions put on the so-called relation of epistemic satisfaction.

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ACCURACY, COMPREHENSIBILITY AND COMPLETENESS EVALUATION OF A FUZZY EXPERT SYSTEM

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488503002181 ◽

2003 ◽

Vol 11 (04) ◽

pp. 445-466 ◽

Cited By ~ 5

Author(s):

PHAYUNG MEESAD ◽

GARY G. YEN

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Expert System ◽

Knowledge Representation ◽

Fuzzy Sets ◽

Fitness Function ◽

Fuzzy Rule ◽

Fuzzy Expert System ◽

Rule Structure ◽

Response Output

Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES) focusing only on its accuracy without considering the comprehensibility may not result in a system that produces understandable expressions. To exploit the transparency characteristics of FES for reasoning in a higher-level knowledge representation, a FES should provide high comprehensibility while preserving its accuracy. The completeness of fuzzy sets and rule structures should also be considered to guarantee that every data point has a response output. This paper proposes some quantitative measures for a FES to determine the degree of the accuracy, the comprehensibility of the fuzzy sets, and the completeness of fuzzy rule structure. These quantitative measures are then used as a fitness function for a genetic algorithm in optimally refining a FES.

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Fuzzy sets and knowledge representation issues

Handbook of Neural Computation ◽

10.1887/0750303123/b365c67 ◽

2004 ◽

Author(s):

Krzysztof J Cios ◽

Witold Pedrycz

Keyword(s):

Knowledge Representation ◽

Fuzzy Sets

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Fuzzy Sets of Type-2

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.1999.p0499 ◽

1999 ◽

Vol 3 (6) ◽

pp. 499-508 ◽

Cited By ~ 1

Author(s):

Robert John ◽

Keyword(s):

Knowledge Representation ◽

Fuzzy Sets ◽

Fuzzy Systems ◽

Medical Application ◽

The Other ◽

Detailed Review

This paper provides a detailed review of the important and growing role that fuzzy sets of type-2 play in knowledge representation and inferencing with fuzzy systems. As well as an up-to-date review of the work in this area, examples are provided that demonstrate how type-2 sets can help with both knowledge representation and inferencing. The paper also reports on the use of type-2 sets in a medical application and summarizes the other type-2 applications reported in the literature.

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Special Issue on Fuzzy Rule Interpolation

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0253 ◽

2011 ◽

Vol 15 (3) ◽

pp. 253-253 ◽

Cited By ~ 2

Author(s):

Szilveszter Kov?cs

Keyword(s):

Knowledge Representation ◽

Fuzzy Sets ◽

Fuzzy Rule ◽

Rule Base ◽

Special Issue ◽

Control Applications ◽

Euclidean Metric ◽

Fuzzy Rule Base ◽

Fuzzy Function ◽

Rule Bases

Fuzzy Rule Interpolation (FRI) methods are well known tools for reasoning in case of insufficient knowledge expressed as sparse fuzzy rule-bases. It also provides a simple way to define fuzzy functions. Despite these advantages, FRI techniques are relatively rarely applied in practice. Enabling sparse fuzzy rule-bases, FRI dramatically simplifies rule-base creation. Regardless of whether the rule-base is generated by a human expert, or automatically from input-output data, the ability to provide reasonable interpolated conclusions even if no rule fires for a given observation, help to concentrate on cardinal actions alone. This reduces the number of rules needed, speeds up parameter optimization and validation steps, and hence simplifies rule-base creation itself. This special issuefs six papers take six different directions in current FRI research. The first introduces the FRI concept and sets up a unified criteria and evaluation system. This work collects the main properties an FRI method generally has to fulfill. The next two papers are related to the constantly important mainstream research on the more and more sophisticated FRI methods, the endeavor of finding the best way for defining a fuzzy valued fuzzy function based on data given in the form of the relation of fuzzy sets, i.e., in fuzzy rules. The second paper introduces a novel FRI method that is able to handle fuzzy observations activating multiple rule antecedents applying the concept of nonlinear fuzzy-valued function. The third paper presents a novel ganalogical-basedh FRI method that rather fits into the traditional FRI research line, improving the n-rule-based gscale and move transformationh FRI to ensure continuous approximate functions. The fourth paper addresses the issue of defining a distance function between fuzzy sets on a domain that is not necessarily Euclidean metric space. In FRI, this takes on the importance if antecedent or consequent domains are non-Euclidean metric spaces. The last two papers discuss direct FRI control applications. One is an example proving that the sparse fuzzy rule-base is an efficient knowledge representation in intelligent control solutions. The last deals with the computational efficiency of implemented FRI methods applied to direct control area, clearly showing that the sparse fuzzy rule-base is not only a convenient way for knowledge representation, but also makes FRI methods possible devices for direct embedded control applications.

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