On the semantics of rule-based expert systems with uncertainty

  • Michael Kifer
  • Ai Li
Logic And Deductive Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 326)


We present a formal semantics for rule-based systems with uncertainty (this field has also become known as “quantitative logic programming”). Unlike previous works, our framework is general enough to accommodate most of the known schemes of reasoning with uncertainty found in the existing expert systems. We provide a rigorous treatment of the issue of evidential independence, and study its impact on the semantics. To the best of our knowledge, this issue has not been addressed before in the literature on quantitative logic programming. In expert systems evidential independence received only an ad hoc treatment, while the approaches found in the theory of evidential reasoning are feasible only in small scale systems. We discuss the problem of query optimization and, as a first step, present a quantitative semi-nave query evaluation algorithm — generalization of a method well-known in deductive databases. Treatment of negation and conflicting evidence based on, so called, support logic is given in the last part of the paper, where we extend the semantics of stratified programs to deal with uncertainty.


Expert System Logic Program Predicate Symbol Query Evaluation Query Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Michael Kifer
    • 1
  • Ai Li
    • 1
  1. 1.Department of Computer ScienceSUNY at Stony BrookU.S.A.

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