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Iterated Revision and Minimal Change of Conditional Beliefs
 JOURNAL OF PHILOSOPHICAL LOGIC
, 1995
"... We describe a model of iterated belief revision that extends the AGM theory of revision to account for the effect of a revision on the conditional beliefs of an agent. In particular, this model ensures that an agent makes as few changes as possible to the conditional component of its belief set. Ado ..."
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Cited by 39 (0 self)
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We describe a model of iterated belief revision that extends the AGM theory of revision to account for the effect of a revision on the conditional beliefs of an agent. In particular, this model ensures that an agent makes as few changes as possible to the conditional component of its belief set. Adopting the Ramsey test, minimal conditional revision provides acceptance conditions for arbitrary rightnested conditionals. We show that problem of determining acceptance of any such nested conditional can be reduced to acceptance tests for unnested conditionals. Thus, iterated revision can be accomplished in a “virtual” manner, using uniterated revision.
Representing partial ignorance
 IEEE Trans. on Systems, Man and Cybernetics
, 1996
"... Ignorance is precious, for once lost it can never be regained. This paper advocates the use of nonpurely probabilistic approaches to higherorder uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decisiondriven and as a consequenc ..."
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Cited by 28 (9 self)
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Ignorance is precious, for once lost it can never be regained. This paper advocates the use of nonpurely probabilistic approaches to higherorder uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decisiondriven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decisiondriven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decisionmaking as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decisionmaking and a nonBayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in nonBayesian settings are reviewed,
Possibilistic Reasoning  A Minisurvey and Uniform Semantics
 Artificial Intelligence
, 1996
"... In this paper, we survey some quantitative and qualitative approaches to uncertainty management based on possibility theory and present a logical framework to integrate them. The semantics of the logic is based on the Dempster's rule of conditioning for possibility theory. It is then shown that clas ..."
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Cited by 5 (0 self)
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In this paper, we survey some quantitative and qualitative approaches to uncertainty management based on possibility theory and present a logical framework to integrate them. The semantics of the logic is based on the Dempster's rule of conditioning for possibility theory. It is then shown that classical modal logic, conditional logic, possibilistic logic, quantitative modal logic and qualitative possibilistic logic are all sublogics of the present logical framework. In this way, we can formalize and generalize some wellknown results about possibilistic reasoning in a uniform semantics. Moreover, our uniform framework is applicable to nonmonotonic reasoning, approximate consequence relation formulation, and partial consistency handling. Key words: Nonclassical logics, possibility theory, conditional possibility, modal logic, conditional logic. 1 Introduction There are essentially two kinds of logical formalisms for reasoning about possibility and necessity. On the one hand, the qua...
A Logic for Reasoning about Fuzzy Truth Values
"... In this paper, we will present a framework for reasoning with vague and uncertain information by fuzzy truthvalued logics. It is shown that possibilistic logic, manyvalued logic, and approximate reasoning can all be embodied in the uniform framework. Keywords Fuzzytruth values, manyvalued lo ..."
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In this paper, we will present a framework for reasoning with vague and uncertain information by fuzzy truthvalued logics. It is shown that possibilistic logic, manyvalued logic, and approximate reasoning can all be embodied in the uniform framework. Keywords Fuzzytruth values, manyvalued logics, possibilistic reasoning, approximate reasoning I. Introduction In the realm of artificial intelligence and knowledgebased systems, one of the central problems is the representation and reasoning of incomplete information. An intelligent agent acting without the full knowledge of the environment would most need the capability of reasoning with incomplete information. It is now commonly believed that there are more than one types of incomplete information so totally different mechanisms would be needed to treat them. An extensive literature has been generated to cope with the problem and various approaches have been proposed. To name some among others, the most notable ones are probabil...
1st International Symposium on Imprecise Probabilities and Their Applications, Ghent,Belgium,29June2July1999 A Logic of Extended Probability
"... This paper shows how the logic of gambles corresponding to Peter Walley's system of Imprecise Probability can be extended to allow gambles involving in nitesimal values and in nite values. This logic can then be used for reasoning with in nitesimal probabilities alongside conventional reasoning with ..."
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This paper shows how the logic of gambles corresponding to Peter Walley's system of Imprecise Probability can be extended to allow gambles involving in nitesimal values and in nite values. This logic can then be used for reasoning with in nitesimal probabilities alongside conventional reasoning with linear constraints on probabilities. The proof theory is shown to be sound and complete for nite input sets.