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.

翻译：多样化、top-k与高质量规划研究关注于为序列决策问题生成解集。此前该领域主要依赖于需要问题实例符号模型的经典规划器。本文提出一种基于蒙特卡洛树搜索（MCTS）的新颖替代方法，使其能够应用于仅具备黑盒仿真模型的问题。我们提出一种在最佳优先顺序下从预生成搜索树中提取有界计划集的方法，以及一种评估搜索树中路径相对质量的度量标准。我们在一个包含隐藏信息的路径规划问题上验证了该方法，并提出了对MCTS算法的改进建议以增加生成计划的多样性。实验结果表明，我们的方法能够在经典规划器无法适用的领域中生成多样且高质量的计划集。