This paper discusses some of the problems related to the representation of uncertain knowledge and to the combination of evidence degrees in rule-based expert systems. Some of the methods proposed in the literature are briefly analysed with particular attention to the Subjective Bayesian Probability (used in PROSPECTOR) and the Confirmation Theory adopted in MYCIN. The paper presents an integrated approach based on Possibility Theory for evaluating the degree of match between the set of conditions occurring in the antecedent of a production rule and the input data, for combining the evidence degree of a fact with the strength of implication of a rule and for combining evidence degrees coming from different pieces of knowledge. The semantics of the logical operators AND and OR in possibility theory and in our approach are compared. Finally, the definitions of some quantifiers like AT LEAST n, AT MOST n, EXACTLY n are introduced.

Evidence Combination in Expert Systems

LESMO, Leonardo;SAITTA, Lorenza;TORASSO, Pietro
1985

Abstract

This paper discusses some of the problems related to the representation of uncertain knowledge and to the combination of evidence degrees in rule-based expert systems. Some of the methods proposed in the literature are briefly analysed with particular attention to the Subjective Bayesian Probability (used in PROSPECTOR) and the Confirmation Theory adopted in MYCIN. The paper presents an integrated approach based on Possibility Theory for evaluating the degree of match between the set of conditions occurring in the antecedent of a production rule and the input data, for combining the evidence degree of a fact with the strength of implication of a rule and for combining evidence degrees coming from different pieces of knowledge. The semantics of the logical operators AND and OR in possibility theory and in our approach are compared. Finally, the definitions of some quantifiers like AT LEAST n, AT MOST n, EXACTLY n are introduced.
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http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WGS-4T5C7JK-6&_user=525216&_coverDate=03%2F31%2F1985&_alid=789424732&_rdoc=1&_fmt=high&_orig=search&_cdi=6830&_sort=d&_docanchor=&view=c&_ct=3&_acct=C000026382&_version=1&_urlVersion=0&_userid=525216&md5=3a321079d3756e30302ac0cc9d934945
expert systems; production rules; approximate reasoning; fuzzy quantifiers
L. LESMO; L. SAITTA; P. TORASSO
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/10460
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