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

2019/04/01 by Yi Wang, Wang, Yi, Joohyung Lee +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies

paper · pdf · doi:10.48550/arxiv.1904.00512

openalex publication_date 2019/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We extend probabilistic action language pBC+ with the notion of utility as 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 to leverage an MDP solver to compute pBC+. 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. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).

Citations

• A Framework for Decision-Theoretic Planning I: Combining the Situation\n Calculus, Conditional Plans, Probability and Utility
• Inductive Policy Selection for First-Order MDPs
• A Markovian Decision Process

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