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.
@article{arxiv.2308.13147,
title = {Diverse, Top-k, and Top-Quality Planning Over Simulators},
author = {Lyndon Benke and Tim Miller and Michael Papasimeon and Nir Lipovetzky},
journal= {arXiv preprint arXiv:2308.13147},
year = {2023}
}
Comments
This paper has been accepted at the 26th European Conference on Artificial Intelligence (ECAI 2023)