Intensional FOL for reasoning about probabilities and probabilistic logic programming. (2018)

• Record Type: Journal Article
• Authors: Majkić, Zoran; Prasad, Bhanu
• Is Part Of: International journal of intelligent information and database systems. Volume 11:Number 1(2018)
• Page Start: 79
• Page End: 96
• Publication Date: 2018
• Subjects: probabilities -- 2-valued intensional first-order logic -- Nilsson's probability structures -- linear inequalities; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

It is important to have a logic, both for computation of probabilities and for reasoning about probabilities, with well-defined syntax and semantics. The current approaches, which are based on Nilsson's probability structures/logics as well as linear inequalities, to reason about probabilities, have some deficiencies. In this research, we have presented a complete revision of those approaches and have shown that the logic for reasoning about probabilities can be naturally embedded into a 2-valued intensional first-order logic (FOL) with intensional abstraction, by avoiding current ad-hoc system composed of two different 2-valued logics: one for the classical propositional logic at a lower-level and a new one, at a higher-level, for probabilistic constraints with probabilistic variables. The theoretical results that are obtained are applied to probabilistic logic programming.