Probabilistic Preference Planning Problem for Markov Decision Processes

doi:10.1109/TSE.2020.3024215

Probabilistic Preference Planning Problem for Markov Decision Processes

2022-05-01

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DOI：10.1109/TSE.2020.3024215

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The classical planning problem aims to find a sequence of permitted actions leading a system to a designed state, i.e., to achieve the system's task. However, in many realistic cases we also have requirements on how to complete the task, indicating that some behaviors and situations are more preferred than others. In this paper, we present the probabilistic preference-based planning problem (P4) for Markov decision processes, where the preferences are defined based on an enriched probabilistic LTL-style logic. We first recall P4Solver, an SMT-based planner computing the preferred plan by reducing the problem to a quadratic programming one previously developed to solve P4. To improve computational efficiency and scalability, we then introduce a new encoding of the probabilistic preference-based planning problem as a multi-objective model checking one, and propose the corresponding planner P4Solver(MO). We illustrate the efficacy of both planners on some selected case studies to show that the model checking-based algorithm is considerably more efficient than the quadratic-programming-based one.

Keywords:

Planning

Markov decision processes

preference

quadratic programming

multi-objective model checking

Probabilistic Preference Planning Problem for Markov Decision Processes

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