A Probabilistic Extension of Action Language BC+

Joohyung Lee, Yi Wang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We present a probabilistic extension of action language. Just like is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call p, is defined as a high-level notation of LPMLN programs - a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in p and computed using an implementation of LPMLN.

Original languageEnglish (US)
Pages (from-to)607-622
Number of pages16
JournalTheory and Practice of Logic Programming
Volume18
Issue number3-4
DOIs
StatePublished - Jul 1 2018

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Artificial Intelligence

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