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By Max Henrion, Ross D. Shachter, Laveen N. Kanal, R. D. Shachter, L. N. Kanal, M. Henrion, J. F. Lemmer

This quantity, like its predecessors, displays the leading edge of analysis at the automation of reasoning less than uncertainty. A extra pragmatic emphasis is clear, for even supposing a few papers tackle basic matters, the bulk handle functional concerns. subject matters comprise the family among substitute formalisms (including possibilistic reasoning), Dempster-Shafer trust services, non-monotonic reasoning, Bayesian and determination theoretic schemes, and new inference thoughts for trust nets. New thoughts are utilized to big difficulties in medication, imaginative and prescient, robotics, and normal language knowing

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Uncertainty in artificial intelligence 5

This quantity, like its predecessors, displays the leading edge of study at the automation of reasoning less than uncertainty. A extra pragmatic emphasis is clear, for even if a few papers handle primary matters, the bulk tackle useful concerns. issues comprise the family members among replacement formalisms (including possibilistic reasoning), Dempster-Shafer trust capabilities, non-monotonic reasoning, Bayesian and choice theoretic schemes, and new inference recommendations for trust nets.

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3 Bayes theorem and reasoning by case Having derived the mathematical structure of legal finite totally ordered probability models, we need deductive rules. In this investigation, we adopted Bayesian Networks [Pearl, 1988] (using the implementation described in [Poole, 1988]) as our scheme of knowledge representation. The inferencing rules required within this scheme are Bayes theorem and reasoning by cases. 3 It takes the form: p(P\QkC) = p(Q\PkC)*p(P\C)/p(Q\C) Reasoning by cases is an inference rule to compute a conditional probability by partitioning the condition into several exclusive situations such that the estimation under each of them is more manageable.

1976) A mathematical theory of evidence. Princeton Univ. Press. Princeton, NJ. 39 SMETS Ph. , DUBOIS D. and PRADE H. ├ęd. Non standard logics for automated reasoning. Academic Press, London pg 253-286. SMETS Ph. , SAITTA L. and YAGER R. Uncertainty and Intelligent Systems. Springer Verlag, Berlin, pg. 17-24 SMETS Ph. (1988c) Transferable belief model versus Bayesian model, in KODRATOFF Y. ECAI1988, Pitman, London, pg. 495-500. SMETS Ph. and KENNES R. (1989a) The transferable belief model: comparison with Bayesian model, (submitted for publication) SMETS Ph.

42 The semantics of TPL is given by 'possible worlds'. Each proposition P is associated with a set of situations or possible worlds S(P) in which P holds. Given Q as evidence, the conditional probability p(P\Q), whose value ranges over the set P , is some measure of the fraction of the set S(Q) that is occupied by the subset S(P&Q). TPL provided minimum constraints for a rational belief model. For our particular domain we thought the following criteria were desirable: R l The domain experts did not believe that they used numerical values for uncertainty.

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