Heuristic Search for Multi-Objective Probabilistic Planning

Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probabilistic planning modelled as a stochastic shortest path (SSP) problem. Here, we extend the reach of heuristic search to a more expressive class of problems, namely multi-objective stochastic shortest paths (MOSSPs), which require computing a coverage set of non-dominated policies. We design new heuristic search algorithms MOLAO* and MOLRTDP, which extend well-known SSP algorithms to the multi-objective case. We further construct a spectrum of domain-independent heuristic functions differing in their ability to take into account the stochastic and multi-objective features of the problem to guide the search. Our experiments demonstrate the benefits of these algorithms and the relative merits of the heuristics.

Authors

• Dillon Z. Chen School of Computing, The Australian National University LAAS-CNRS, Université de Toulouse
• Felipe Trevizan Australian National University
• Sylvie Thiébaux School of Computing, The Australian National University LAAS-CNRS, Université de Toulouse

Keywords

• PRS: Planning under Uncertainty
• PRS: Planning with Markov Models (MDPs, POMDPs)