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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.
Aleksandar Perović, Dragan Doder
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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.
Aleksandar Perović, Dragan Doder
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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.
Victor Lesser, J. W. Guan
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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.
Victor Lesser, J. W. Guan
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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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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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Nanocomputing with Probabilistic Logic
2006 Sixth IEEE Conference on Nanotechnology, 2006This presentation considers the impact on logic design and computing of the fundamental unreliability of nanoscale device technologies. In general, these technologies will provide implementations of logic gates and circuits where logic levels are “0” or “1” with some probability related to the error rates of gates and interconnect.
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Probabilistic forcing in quantum logics [PDF]
International Journal of Theoretical Physics, 1993 It is shown that orthomodular lattice can be axiomatized as an ortholattice with a unique operation of identity (bi-implication) instead of the operation of implication and a corresponding algebraic unified quantum logic is formulated. A statistical YES-NO physical interpretation of the quantum logical propositions is then provided to establish a ...
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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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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 Automata and Probabilistic Logic
2012We present a monadic second-order logic which is extended by an expected value operator and show that this logic is expressively equivalent to probabilistic automata for both finite and infinite words. We give possible syntax extensions and an embedding of our probabilistic logic into weighted MSO logic. We further derive decidability results which are
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The logic of probabilistic knowledge
Philosophical Studies, 2019Sarah Moss’ thesis that we have probabilistic knowledge is from some perspectives unsurprising and from other perspectives hard to make sense of. The thesis is potentially transformative, but not yet elaborated in sufficient detail for epistemologists.
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Probabilistic Logic and Induction
Journal of Logic and Computation, 2005We give a probabilistic interpretation of first-order formulas based on Valiants 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 rise to a
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The probabilistic foundations of logic
Proceedings. The Nineteenth International Symposium on Multiple-Valued Logic, 2003Summary form only given, as follows. A conditional version of the Komolgoroff axioms for probability theory is developed, and it is shown that the resulting theory can serve as a formal semantics for any logic in which the notion of maximally consistent set is definable, provided the probability functions are defined over the power set of the maximally
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