Results 221 to 230 of about 14,132 (263)

Probabilistic logic

Artificial Intelligence, 1986
Als Verallgemeinerung der klassischen Logik wird eine Logik mit Wahrheitswerten zwischen 0 und 1 vorgestellt. Techniken zur Berechnung der Wahrscheinlichkeit von Folgerungen und bedingten Wahrscheinlichkeiten, u.a. eine approximative Methode, werden angegeben.
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Probabilistic Stit Logic

2011
We define an extension of stit logic that encompasses subjective probabilities representing beliefs about simultaneous choice exertion of other agents. This semantics enables us to express that an agent sees to it that a condition obtains under a minimal chance of success.
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Probabilistic Dynamic Epistemic Logic

Journal of Logic, Language and Information, 2003
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Probabilistic Logic and Induction

Journal of Logic and Computation, 2005
Summary: We give a probabilistic interpretation of first-order formulas based on Valiant's model of pac-learning. We study the resulting notion of probabilistic or approximate truth and take some first steps in developing its model theory. In particular we show that every fixed error parameter determining the precision of universal quantification gives
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Probabilistic IF Logic

2013
In a seminal paper Goldfarb (1979) points out that ”The connection between quantifiers and choice functions or, more precisely, between quantifier-dependence and choice functions, is at the heart of how classical logicians in the twenties viewed the nature of quantification.” (Goldfarb 1979, p. 357).
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Probabilistic Entailment and a Non-Probabilistic Logic

Logic Journal of IGPL, 2003
Let \(L\) be a finite language of propositional logic. We say that \(\Gamma\), a set of \(m\) sentences of \(L\), \(\{\theta_1, \ldots \theta_m\}\) with probabilities \(\{\eta_1, \ldots, \eta_m\}\), \((\vec{e},\zeta)\)-entails \(\psi\) provided that the probability of \(\psi\) is at least \(\zeta\) for all probability functions \(P\) for which \(P ...
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Probabilistic Temporal Logics

2020
This chapter presents the proof-theoretical and model-theoretical approaches to reasoning about time and probability. Three different ways of combining probabilistic and temporal modalities are presented, and well defined syntax and corresponding semantics is provided for every formalism.
Doder, Dragan, Perovic, Aleksandar
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On Probabilistic Logic

1990
Because many artificial intelligence applications require the ability to reason with uncertain knowledge, it is important to seek appropriate generalizations of logic for that case. Nils J. Nilsson [1] presented a semantical generalization of logic in which the truth values of sentences are probability values between 0 and 1.
Jiwen Guan, Victor R. Lesser
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