P-log: refinement and a new coherency condition

This paper focuses on the investigation and improvement of knowledge representation language P-log that allows for both logical and probabilistic reasoning. We refine the definition of the language by eliminating some ambiguities and incidental decisions made in its original version and slightly mod...

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Bibliographic Details
Published in:Annals of mathematics and artificial intelligence Vol. 86; no. 1-3; pp. 149 - 192
Main Authors: Balai, Evgenii, Gelfond, Michael, Zhang, Yuanlin
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.07.2019
Springer
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Boolean
Cognition & reasoning
Complex Systems
Computer Science
Knowledge representation
Language
Mathematical functions
Mathematics
Semantics
Syntax
68
60
Probabilistic inference
Answer set programming
Knowledge representation
ISSN:1012-2443, 1573-7470
Online Access:Get full text
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Summary:This paper focuses on the investigation and improvement of knowledge representation language P-log that allows for both logical and probabilistic reasoning. We refine the definition of the language by eliminating some ambiguities and incidental decisions made in its original version and slightly modify the formal semantics to better match the intuitive meaning of the language constructs. We also define a new class of coherent (i.e., logically and probabilistically consistent) P-log programs which facilitates their construction and proofs of correctness. There are a query answering algorithm, sound for programs from this class, and a prototype implementation which, due to their size, are not included in the paper. They, however, can be found in the dissertation of the first author.
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ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-019-09620-2