P-log: refinement and a new coherency condition

Evgenii Balai, Michael Gelfond, Yuanlin Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)149-192
Number of pages44
JournalAnnals of Mathematics and Artificial Intelligence
Volume86
Issue number1-3
DOIs
StatePublished - Jul 1 2019

Keywords

  • Answer set programming
  • Knowledge representation
  • Probabilistic inference

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