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Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language $p{\cal BC}$+

Published online by Cambridge University Press:  23 December 2020

YI WANG Open the ORCID record for YI WANG [Opens in a new window]  and
JOOHYUNG LEE Open the ORCID record for JOOHYUNG LEE [Opens in a new window]
YI WANG
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA (e-mails: ywang485@asu.edu, joolee@asu.edu)
JOOHYUNG LEE
Affiliation:
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA (e-mails: ywang485@asu.edu, joolee@asu.edu)
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Abstract

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

Keywords

answer set programmingaction languageMarkov decision process
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 3 , May 2021 , pp. 348 - 371
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

We are grateful to the anonymous referees for their useful comments and to Siddharth Srivastava, Zhun Yang, and Yu Zhang for helpful discussions. This work was partially supported by the National Science Foundation under Grant IIS-1815337.

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