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

Computing and Augmented Intelligence, School of (IAFSE-SCAI)

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

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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N2 - 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.

AB - 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+

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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