Soft Computing for Intelligent Knowledge-based Systems

Knowledge-based systems are founded on the idea that knowledge should be declarative, so that it can be easily read, understood, and altered by a human user as well as by a machine. Logic fulfils these criteria, and logic programming has been widely used for implementing knowledge-based systems. One major shortcoming of logic programming is the lack of a mechanism to deal with the uncertainty inherent in many knowledge-based systems. Soft computing is a key technology for the management of uncertainty, although so far its major successes have been centred on fuzzy control rather than higher level information management. This paper outlines some of the issues related to the area of soft computing in knowledge-based systems, and suggests some simple problems to test the capabilities of software. Fril is discussed as an implementation language for knowledge-based systems involving uncertainty, and some of its applications are outlined.

[1]  Trevor P Martin,et al.  Modelling with words using Cartesian granule features , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[2]  James J. Buckley,et al.  A fuzzy expert system , 1986 .

[3]  J. H. Sims-Williams,et al.  A computer-controlled expert system for orthodontic advice , 1987, British Dental Journal.

[4]  Lotfi A. Zadeh,et al.  The Roles of Fuzzy Logic and Soft Computing in the Conception, Design and Deployment of Intelligent Systems , 1997, Software Agents and Soft Computing.

[5]  Hung T. Nguyen,et al.  On Random Sets and Belief Functions , 1978, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[6]  James F. Baldwin.  Knowledge from data using fuzzy methods , 1996, Pattern Recognit. Lett..

[7]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[8]  Trevor P Martin,et al.  Fuzzy processing of hydrophone sounds , 1996, Fuzzy Sets Syst..

[9]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[10]  Jon H Sims Williams,et al.  An orthodontic expert system , 1989 .

[11]  S. K. Morton,et al.  A knowledge based expert system for laminated composite strut design , 1991, The Aeronautical Journal (1968).

[12]  Jf Baldwin,et al.  A fuzzy data browser in Fril , 1995 .

[13]  Didier Dubois,et al.  Information engineering and fuzzy logic , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[14]  J. Baldwin.  A calculus for mass assignments in evidential reasoning , 1994 .

[15]  Trevor P Martin,et al.  Fuzzy modelling in an intelligent data browser , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[16]  Trevor P Martin,et al.  Software agents and soft computing : concepts and applications , 1997 .

[17]  Trevor P Martin,et al.  The implementation of fprolog—a fuzzy prolog interpreter , 1987 .

[18]  J. A. Robinson,et al.  A Machine-Oriented Logic Based on the Resolution Principle , 1965, JACM.

[19]  Liya Ding,et al.  Fundamentals of Fuzzy Prolog , 1989, Int. J. Approx. Reason..

[20]  Trevor P Martin,et al.  Fast operations on fuzzy sets in the abstract Fril machine , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[21]  前田 博,et al.  Didier Dubois and Henri Prade Fuzzy sets in approximate reasoning, Part 1 : Inference with possibility distributions Fuzzy Sets and Systems, vol.40,pp143-202,1991 , 1995 .

[22]  Trevor P Martin,et al.  Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence , 1995 .

[23]  Trevor P Martin,et al.  Incremental learning in a Fril-based odour classifier , 1998 .

[24]  James F. Baldwin,et al.  An Abstract Mechanism for Handling Uncertainty , 1990, IPMU.

[25]  Jf Baldwin,et al.  Efficient algorithms for semantic unification , 1996 .

[26]  Deyi Li,et al.  A Fuzzy Prolog Database System , 1990 .

[27]  D. Dubois,et al.  Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions , 1999 .

[28]  Robert A. Kowalski,et al.  Logic for problem solving , 1982, The computer science library : Artificial intelligence series.

[29]  James F. Baldwin,et al.  A New Approach to Inference Under Uncertainty for Knowledge Based Systems , 1991, ECSQARU.

[30]  J. F. Baldwin,et al.  Evidential Reasoning Under Probabilistic and Fuzzy Uncertainties , 1992 .

[31]  Ronald R. Yager,et al.  ON DIFFERENT CLASSES OF LINGUISTIC VARIABLES DEFINED VIA FUZZY SUBSETS , 1984 .

[32]  James F. Baldwin,et al.  Basic Concepts of a Fuzzy Logic Data Browser with Applications , 1997, Software Agents and Soft Computing.

[33]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[34]  James F. Baldwin,et al.  Fuzzy Sets, Fuzzy Clustering and Fuzzy Rules in AI , 1993, Fuzzy Logic in Artificial Intelligence.

[35]  T. P. Martin,et al.  Logic Programming and Soft Computing , 1998 .

[36]  Richard C. T. Lee.  Fuzzy Logic and the Resolution Principle , 1971, JACM.

[37]  Trevor P Martin,et al.  The Intelligent Manual in FRIL , 1993 .