Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+
This work provides a more efficient representation method for MDPs in logic programming, which is incremental as it builds upon existing pBC+ and LPMLN frameworks.
The authors extended the probabilistic action language pBC+ with utility concepts from decision theory, enabling a succinct and elaboration-tolerant representation of Markov Decision Processes (MDPs) and leading to the development of the pbcplus2mdp system for computing optimal policies using MDP solvers.
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).