Preference Planning for Markov Decision Processes

Authors

  • Meilun Li Beihang University
  • Zhikun She Beihang University
  • Andrea Turrini Institute of Software, Chinese Academy of Sciences
  • Lijun Zhang Institute of Software, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v29i1.9654

Keywords:

Markov Decision Process, Planning Problem, Probabilistic Planning, User Preference, Probabilistic LTL

Abstract

The classical planning problem can be enriched with quantitative and qualitative user-defined preferences on how the system behaves on achieving the goal. In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic. We develop P4Solver, an SMT-based planner computing the preferred plan by reducing the problem to quadratic programming problem, which can be solved using SMT solvers such as Z3. We illustrate the framework by applying our approach on two selected case studies.

AAAI 2015 Proceedings Cover

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Published

2015-03-04

How to Cite

Li, M., She, Z., Turrini, A., & Zhang, L. (2015). Preference Planning for Markov Decision Processes. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9654

Issue

Vol. 29 No. 1 (2015): Twenty-Ninth AAAI Conference on Artificial Intelligence

Section

Main Track: Planning and Scheduling