Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language +

Y. I. Wang, Joohyung Lee

Research output: Contribution to journalArticlepeer-review

Abstract

We extend probabilistic action language + with the notion of utility in decision theory. The semantics of the extended + can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LPMLN. Alternatively, the semantics of + can also be defined in terms of Markov decision process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as leveraging an MDP solver to compute a + action description. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a + action description using an MDP solver.

Original languageEnglish (US)
JournalTheory and Practice of Logic Programming
DOIs
StateAccepted/In press - 2020

Keywords

  • action language
  • answer set programming
  • Markov decision process

ASJC Scopus subject areas

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

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