用流程挖掘解析多智能体蒙特卡洛树搜索的决策逻辑
Exploring Explainable Multi-agent MCTS-minimax Hybrids in Board Game Using Process Mining
- 在蒙特卡洛模拟中嵌入浅层极小极大搜索，提升策略覆盖
- 通过流程挖掘揭示3人跳棋中智能体的行为模式与决策路径
- 适合关注AI可解释性与博弈策略分析的研究者
蒙特卡洛树搜索（MCTS）是一类基于采样的搜索算法，广泛应用于顺序决策任务中的在线规划，并构成了近年来人工智能进展的核心。由于其模拟大量可能未来并生成复杂搜索树，开发者和用户难以理解MCTS智能体的行为。本文开展对MCTS决策过程解释性的初步研究。传统MCTS存在过度选择性问题，易遗漏关键着法并陷入战术陷阱。全宽极小极大搜索可作为解决方案。本研究将浅层极小极大搜索引入多智能体MCTS的模拟阶段，并利用流程挖掘技术分析3人跳棋场景下智能体的策略行为。
原文摘要 · Abstract (English)
Monte-Carlo Tree Search (MCTS) is a family of sampling-based search algorithms widely used for online planning in sequential decision-making domains and at the heart of many recent advances in artificial intelligence. Understanding the behavior of MCTS agents is difficult for developers and users due to the frequently large and complex search trees that result from the simulation of many possible futures, their evaluations, and their relationships. This paper presents our ongoing investigation into potential explanations for the decision-making and behavior of MCTS. A weakness of MCTS is that it constructs a highly selective tree and, as a result, can miss crucial moves and fall into tactical traps. Full-width minimax search constitutes the solution. We integrate shallow minimax search into the rollout phase of multi-agent MCTS and use process mining technique to explain agents' strategies in 3v3 checkers.
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