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Probabilistic Logic: Many-valuedness and Intensionality

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arxiv 1103.0676 v1 pith:PKNQQ2A2 submitted 2011-03-03 cs.LO

classification cs.LO
keywords logicprobabilisticprobabilitiescurrentnilssonprobabilitypropositionalreasoning
verification ladder T0 review T1 audit T2 compute T3 formal
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The probability theory is a well-studied branch of mathematics, in order to carry out formal reasoning about probability. Thus, it is important to have a logic, both for computation of probabilities and for reasoning about probabilities, with a well-defined syntax and semantics. Both current approaches, based on Nilsson's probability structures/logics, and on linear inequalities in order to reason about probabilities, have some weak points. In this paper we have presented the complete revision of both approaches. We have shown that the full embedding of Nilsson'probabilistic structure into propositional logic results in a truth-functional many-valued logic, differently from Nilsson's intuition and current considerations about propositional probabilistic logic. Than we have shown that the logic for reasoning about probabilities can be naturally embedded into a 2-valued intensional FOL with intensional abstraction, by avoiding current ad-hoc system composed of two different 2-valued logics: one for the classical propositional logic at lower-level, and a new one at higher-level for probabilistic constraints with probabilistic variables. The obtained theoretical results are applied to Probabilistic Logic Programming.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL

    cs.AI 2026-07 reject novelty 3.0 of 10

    A theoretical paper defines global and local 'symmetry transformations' to assign probabilities to unknown sentences in a Belnap four-valued intensional logic, with no implementation or validation.

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