Automatic refinement of knowledge bases with fuzzy rules

Abstract The knowledge-acquisition bottleneck obstructs the development of expert systems. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem. The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II automatically. HERES employs heuristics and analytical methods to guide its generation of plausible refinements. The functionality and effectiveness of HERES are verified through various case studies. It has been verified that HERES can successfully refine knowledge bases. The refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms such as ID3 15–18 , AQ11, and INDUCE 14, 19, 20 because HERES' methods are currently unique in processing inexact examples and creating approximate rules.

[1]  Kwong-Sak Leung,et al.  Fuzzy concepts in expert systems , 1988, Computer.

[2]  Zbigniew W. Ras,et al.  Methodologies for Intelligent Systems , 1991, Lecture Notes in Computer Science.

[3]  H. Prade,et al.  FUZZY PATTERN MATCHING , 1982 .

[4]  K. S. Leung,et al.  The Implementation of Fuzzy Knowledge-Based System Shells , 1987 .

[5]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

[7]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[8]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[9]  Sholom M. Weiss,et al.  Automatic Knowledge Base Refinement for Classification Systems , 1988, Artif. Intell..

[10]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[11]  Richard M. Keller,et al.  Defining Operationality for Explanation-Based Learning , 1987, Artificial Intelligence.

[12]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[13]  Allen Ginsberg,et al.  Automatic Refinement of Expert System Knowledge Bases , 1988 .

[14]  Ryszard S. Michalski,et al.  Discovering Classification Rules Using variable-Valued Logic System VL1 , 1973, IJCAI.

[15]  Peter G. Politakis,et al.  Empirical analysis for expert systems , 1985 .

[16]  Sholom M. Weiss,et al.  Using Empirical Analysis to Refine Expert System Knowledge Bases , 1984, Artif. Intell..

[17]  J. R. Quinlan.  Probabilistic decision trees , 1990 .