Diverse, Top-k, and Top-Quality Planning Over Simulators

• URL: http://arxiv.org/abs/2308.13147v1
• Date: Fri, 25 Aug 2023 02:55:19 GMT
• Title: Diverse, Top-k, and Top-Quality Planning Over Simulators
• Authors: Lyndon Benke, Tim Miller, Michael Papasimeon, and Nir Lipovetzky
• Abstract summary: This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS) We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.
• Score: 9.924007495979582
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.

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