On the combination of uncertain or imprecise pieces of information in rule-based systems-A discussion in the framework of possibility theory

Abstract This paper is devoted to a general discussion of the combination of distinct imprecise or uncertain pieces of information pertaining to the same logical or numerical variable in rule-based expert systems using numerical approximate reasoning techniques. The reasons that the results of this combination may be meaningless are considered in detail. In particular, the presence of implicit default assumptions in the condition part of a rule is discussed and coped with. Moreover, the paper studies why the conclusions obtained by the most specific rule must be definitely preferred to conclusions derived using more general rules. Specificity ordering is defined within the framework of possibility theory.

[1]  Eric Horvitz,et al.  The myth of modularity in rule-based systems for reasoning with uncertainty , 1986, UAI.

[2]  D. Dubois,et al.  FUZZY LOGICS AND THE GENERALIZED MODUS PONENS REVISITED , 1984 .

[3]  Didier Dubois,et al.  On fuzzy syllogisms , 1988, Comput. Intell..

[4]  Henri Prade,et al.  Approximate Reasoning in a Rule-Based Expert System using Possibility Theory: A Case Study , 1986, IFIP Congress.

[5]  Lotfi A. Zadeh,et al.  Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Didier Dubois,et al.  The principle of minimum specificity as a basis for evidential reasoning , 1986, IPMU.

[7]  Bart Kosko,et al.  Fuzzy knowledge combination , 1986, Int. J. Intell. Syst..

[8]  Henri Prade,et al.  A Quantitative Approach to Approximate Reasoning in Rule-based Expert Systems , 1988 .

[9]  J. Lebailly,et al.  Use of fuzzy logic in a rule-based system in petroleum geology , 1987 .

[10]  David S. Touretzky,et al.  Implicit Ordering of Defaults in Inheritance Systems , 1984, AAAI.

[11]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Lotfi A. Zadeh,et al.  A Theory of Approximate Reasoning , 1979 .

[13]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[14]  Matthew L. Ginsberg,et al.  Non-Monotonic Reasoning Using Dempster's Rule , 1984, AAAI.

[15]  David Poole,et al.  On the Comparison of Theories: Preferring the Most Specific Explanation , 1985, IJCAI.

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

[17]  Raymond Reiter,et al.  SOME REPRESENTATIONAL ISSUES IN DEFAULT REASONING , 1980 .

[18]  Didier Dubois,et al.  Weighted minimum and maximum operations in fuzzy set theory , 1986, Inf. Sci..

[19]  Henri Prade,et al.  Default and Inexact Reasoning with Possibility Degrees , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Henri Prade,et al.  On the Problems of Representation and Propagation of Uncertainty in Expert Systems , 1985, Int. J. Man Mach. Stud..

[21]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[22]  Didier Dubois,et al.  Default Reasoning and Possibility Theory , 1988, Artif. Intell..

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

[24]  M. Minsky.  Jokes and the Logic of the Cognitive Unconscious , 1980 .

[25]  Lokendra Shastri,et al.  Evidential Reasoning in Semantic Networks: A Formal Theory , 1985, IJCAI.