标题： 结合大型语言模型与基于逻辑的框架来解释MCTS 显示英文标题

标题： Combining LLMs with Logic-Based Framework to Explain MCTS

摘要： 针对人工智能（AI）在顺序规划中的信任缺失问题，我们设计了一个基于计算树逻辑（CTL）引导的大语言模型（LLM）的自然语言解释框架，旨在用于蒙特卡洛树搜索（MCTS）算法。 由于其搜索树的复杂性，MCTS通常被认为难以理解，但我们的框架足够灵活，能够处理各种自由形式的后验查询和以MCTS及应用领域马尔可夫决策过程（MDP）为中心的知识型查询。 通过将用户查询转化为逻辑和变量声明，我们的框架确保从搜索树获得的证据在事实层面上与底层环境动态和实际随机控制过程中存在的任何约束保持一致。 我们通过定量评估对该框架进行了严格验证，在准确性与事实一致性方面表现出色。 显示英文摘要

摘要： In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into logic and variable statements, our framework ensures that the evidence obtained from the search tree remains factually consistent with the underlying environmental dynamics and any constraints in the actual stochastic control process. We evaluate the framework rigorously through quantitative assessments, where it demonstrates strong performance in terms of accuracy and factual consistency.