Titlebook: Knowledge Representation and Defeasible Reasoning; Henry E. Kyburg,Ronald P. Loui,Greg N. Carlson Book 1990 Kluwer Academic Publishers 199

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Knowledge Representation and Defeasible Reasoning978-94-009-0553-5Series ISSN 0924-0780

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A Framework for Reasoning with Defaultsounts. Classical logic for instance, cannot accommodate belief revision: new information can only add new theorems. Probability theory, on the other hand, has difficulties in accommodating belief commitment: propositions are believed only to a certain degree which dynamically changes with the acquisition of new information.

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Speaker Plans, Linguistic Contexts, and Indirect Speech Actsthis example:.The sentence “I have to study for an exam” is not lexically tied to turning down proposals to go to the movies. There is no sense in which this is part of its meaning. However, in this context B uses it to do just this.

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Conditionals and Conditional Probabilities: Three Triviality Theoremshen .’, ‘.(.)’ for ‘The conditional probability of . on (or given) .’, and ‘.’ for ‘?(. & ?.)’, . here some fixed but arbitrary statement. The conditionals Jeffrey advocates then are those which issue from, and hence for which . meets, this constraint.hence this one

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Defeasible Specification of Utilitiesequire probabilities, utilities, and a set of alternative courses of action. Presumably, work on evidential reasoning will deliver probabilities suitable for AI’s use, and planning has already discovered various ways of representing and composing sequences of primitive actions. This leaves the specification of utilities of outcome states.

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Two Perspectives on Consensus for (Bayesian) Inference and DecisionsIn this paper I discuss questions of consensus among Bayesian investigators, from two perspectives:.and

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