The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Jerome Lang - One of the best experts on this subject based on the ideXlab platform.
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from ordering based nonmonotonic reasoning to Conditional Logics
Artificial Intelligence, 1994Co-Authors: Luis Fariñas Del Cerro, Andreas Herzig, Jerome LangAbstract:As Ga¨rdenfors and Makinson have recently shown, a nonmonotonic inference relation can be generated from a total pre-ordering on the set of formulas, or equivalently from an uncertainty valuation. We build here on these results; we include the pre-ordering in the language by introducing a Conditional operator, and we extend the generation of a nonmonotonic inference relation by allowing the use of incompletely specified pre-orderings. This allows effective procedures for computing nonmonotonic inferences by translating nonmonotonic reasoning into deduction in a Conditional Logic. —Authors' Abstract
Christoph Beierle - One of the best experts on this subject based on the ideXlab platform.
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KI - Descriptor Revision for Conditionals: Literal Descriptors and Conditional Preservation
Lecture Notes in Computer Science, 2020Co-Authors: Kai Sauerwald, Jonas Haldimann, Martin Von Berg, Christoph BeierleAbstract:Descriptor revision by Hansson is a framework for addressing the problem of belief change. In descriptor revision, different kinds of change processes are dealt with in a joint framework. Individual change requirements are qualified by specific success conditions expressed by a belief descriptor, and belief descriptors can be combined by Logical connectives. This is in contrast to the currently dominating AGM paradigm shaped by Alchourron, Gardenfors, and Makinson, where different kinds of changes, like a revision or a contraction, are dealt with separately. In this article, we investigate the realisation of descriptor revision for a Conditional Logic while restricting descriptors to the conjunction of literal descriptors. We apply the principle of Conditional preservation developed by Kern-Isberner to descriptor revision for Conditionals, show how descriptor revision for Conditionals under these restrictions can be characterised by a constraint satisfaction problem, and implement it using constraint Logic programming. Since our Conditional Logic subsumes propositional Logic, our approach also realises descriptor revision for propositional Logic.
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on lifted inference for a relational probabilistic Conditional Logic with maximum entropy semantics
Foundations of Information and Knowledge Systems, 2012Co-Authors: Annika Kramer, Christoph BeierleAbstract:When extending probabilistic Logic to a relational setting, it is desirable to still be able to use efficient inference mechanisms developed for the propositional case. In this paper, we investigate the relational probabilistic Conditional Logic FO-PCL whose semantics employs the principle of maximum entropy. While in general, this semantics is defined via the ground instances of the rules in an FO-PCL knowledge base R, the maximum entropy model can be computed on the level of rules rather than on the level of instances of the rules if R is parametrically uniform, thus providing lifted inference.We elaborate in detail the reasons precluding R from being parametrically uniform. Based on this investigation, we derive a new syntactic criterion for parametric uniformity and develop an algorithm that transforms any FO-PCL knowledge base R into an equivalent knowledge base R′ that is parametrically uniform.
Joseph Y Halpern - One of the best experts on this subject based on the ideXlab platform.
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first order Conditional Logic for default reasoning revisited
ACM Transactions on Computational Logic, 2000Co-Authors: Nir Friedman, Joseph Y Halpern, Daphne KollerAbstract:Conditional Logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order Conditional Logic. We show that, as for first-order probabilistic Logic, it is important not to confound statistical Conditionals over the domain (such as “most birds fly”), and subjective Conditionals over possible worlds (such as “I believe that Tweety is unlikely to fly”). We then address the issue of ascribing semantics to first-order Conditional Logic. As in the propositional case, there are many possible semantics. To study the problem in a coherent way, we use plausibility structures. These provide us with a general framework in which many of the standard approaches can be embedded. We show that while these standard approaches are all the same at the propositional level, they are significantly different in the context of a first-order language. Furthermore, we show that plausibilities provide the most natural extension of Conditional Logic to the first-order case:we provide a sound and complete axiomatization that contains only the KLM properties and standard axioms of first-order modal Logic. We show that most of the other approaches have additional properties, which result in an inappropriate treatment of an infinitary version of the lottery paradox.
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hypothetical knowledge and counterfactual reasoning
Theoretical Aspects of Rationality and Knowledge, 2000Co-Authors: Joseph Y HalpernAbstract:Samet introduced a notion of hypothetical knowledge and showed how it could be used to capture the type of counterfactual reasoning necessary to force the backwards induction solution in a game of perfect information. He argued that while hypothetical knowledge and the extended information structures used to model it bear some resemblance to the way philosophers have used Conditional Logic to model counterfactuals, hypothetical knowledge cannot be reduced to Conditional Logic together with epistemic Logic. Here it is shown that in fact hypothetical knowledge can be captured using the standard counterfactual operator ">" and the knowledge operator "K", provided that some assumptions are made regarding the interaction between the two. It is argued, however, that these assumptions are unreasonable in general, as are the axioms that follow from them. Some implications for game theory are discussed.
Luis Fariñas Del Cerro - One of the best experts on this subject based on the ideXlab platform.
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from ordering based nonmonotonic reasoning to Conditional Logics
Artificial Intelligence, 1994Co-Authors: Luis Fariñas Del Cerro, Andreas Herzig, Jerome LangAbstract:As Ga¨rdenfors and Makinson have recently shown, a nonmonotonic inference relation can be generated from a total pre-ordering on the set of formulas, or equivalently from an uncertainty valuation. We build here on these results; we include the pre-ordering in the language by introducing a Conditional operator, and we extend the generation of a nonmonotonic inference relation by allowing the use of incompletely specified pre-orderings. This allows effective procedures for computing nonmonotonic inferences by translating nonmonotonic reasoning into deduction in a Conditional Logic. —Authors' Abstract
Wilhelm Rodder - One of the best experts on this subject based on the ideXlab platform.
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Conditional Logic and the principle of entropy
Artificial Intelligence, 2000Co-Authors: Wilhelm RodderAbstract:Abstract The Conditional three-valued Logic of Calabrese is applied to the language L ∗ of Conditionals on propositional variables with finite domain. The Conditionals in L ∗ serve as a means for the construction and manipulation of probability distributions respecting the Principle of Maximum Entropy and of Minimum Relative Entropy. This principle allows a sound inference even in the presence of uncertain evidence. The inference is directed, it respects a probabilistic version of Modus Ponens—not of Modus Tollens—, it permits transitive chaining and supports a cautious monotony. Conjunctive, Conditional and material deduction are manageable in this probabilistic Logic, too. The concept is not merely theoretical, but enables large-scale applications in the expert system-shell SPIRIT.