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arXiv · 2303.14363

Heuristic Search for Multi-Objective Probabilistic Planning

Dillon Chen·Felipe Trevizan·Sylvie Thiébaux
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Abstract

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.

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Dillon Chen, Felipe Trevizan, Sylvie Thiébaux. 2023-03-25. Heuristic Search for Multi-Objective Probabilistic Planning. https://doi.org/10.1609/aaai.v37i10.26409

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