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

Yi Wang; Joohyung Lee

doi:10.1007/978-3-030-20528-7_17

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

Yi Wang, Joohyung Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

We extend probabilistic action language pBC+ with the notion of utility in decision theory. The semantics of the extended pBC+ can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LP^MLN. Alternatively, the semantics of pBC+ 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 pBC+ action description. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a pBC+ action description using an MDP solver.

Original language	English (US)
Title of host publication	Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings
Editors	Yuliya Lierler, Stefan Woltran, Marcello Balduccini
Publisher	Springer Verlag
Pages	224-238
Number of pages	15
ISBN (Print)	9783030205270
DOIs	https://doi.org/10.1007/978-3-030-20528-7_17
State	Published - 2019
Event	15th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2019 - Philadelphia, United States Duration: Jun 3 2019 → Jun 7 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11481 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2019
Country/Territory	United States
City	Philadelphia
Period	6/3/19 → 6/7/19

Keywords

Action language
Answer set programming
Markov Decision Process

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-20528-7_17

Cite this

Wang, Y., & Lee, J. (2019). Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+. In Y. Lierler, S. Woltran, & M. Balduccini (Eds.), Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings (pp. 224-238). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11481 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-20528-7_17

Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+. / Wang, Yi; Lee, Joohyung.
Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings. ed. / Yuliya Lierler; Stefan Woltran; Marcello Balduccini. Springer Verlag, 2019. p. 224-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11481 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, Y & Lee, J 2019, Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+. in Y Lierler, S Woltran & M Balduccini (eds), Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11481 LNAI, Springer Verlag, pp. 224-238, 15th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2019, Philadelphia, United States, 6/3/19. https://doi.org/10.1007/978-3-030-20528-7_17

Wang Y, Lee J. Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+. In Lierler Y, Woltran S, Balduccini M, editors, Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings. Springer Verlag. 2019. p. 224-238. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-20528-7_17

Wang, Yi ; Lee, Joohyung. / Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+. Logic Programming and Nonmonotonic Reasoning - 15th International Conference, LPNMR 2019, Proceedings. editor / Yuliya Lierler ; Stefan Woltran ; Marcello Balduccini. Springer Verlag, 2019. pp. 224-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We extend probabilistic action language pBC+ with the notion of utility in decision theory. The semantics of the extended pBC+ can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language LPMLN. Alternatively, the semantics of pBC+ 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 pBC+ action description. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a pBC+ action description using an MDP solver.",

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

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Keywords

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