用大模型+逻辑框架解释强化学习中的搜索过程。
Combining LLMs with Logic-Based Framework to Explain MCTS
- 将用户提问转为逻辑表达，确保解释与环境动态一致
- 在准确性和事实一致性上表现优异，支持自由提问
- 适合需要可解释性的强化学习应用开发者
针对序列规划中人工智能缺乏可信度的问题，我们设计了一种基于计算树逻辑的大型语言模型（LLM）自然语言解释框架，用于蒙特卡洛树搜索（MCTS）算法。由于搜索树结构复杂，MCTS通常难以解释，而本框架具备灵活性，可应对多种自由形式的事后查询和基于知识的提问，聚焦于MCTS及应用领域的马尔可夫决策过程（MDP）。通过将用户查询转化为逻辑与变量语句，框架确保从搜索树中获取的证据在事实层面与底层环境动态及实际随机控制过程中的约束保持一致。我们通过定量评估验证了该框架，结果显示其在准确性和事实一致性方面表现强劲。
原文摘要 · Abstract (English)
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.
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