用流程挖掘+大模型解释多智能体博弈中混合搜索的决策逻辑
M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs
- 在蒙特卡洛树搜索中引入浅层广度极大极小搜索，增强策略深度
- 通过流程挖掘提取智能体行为路径，生成可读性解释
- 适合需要理解复杂博弈策略的开发者与研究人员
蒙特卡洛树搜索（MCTS）是序列决策领域常用的基础采样搜索算法。尽管在人工智能中取得显著进展，但理解MCTS智能体的行为仍具挑战，主要源于大量未来状态模拟产生的复杂搜索树及其内在关联。标准MCTS依赖高度选择性的树构建，可能遗漏关键走法并易受战术陷阱影响。为此，我们将在多智能体MCTS的仿真阶段引入浅层、全宽的极大极小搜索，以提升战略深度。此外，为揭示决策逻辑，提出M2-PALE框架（MCTS–Minimax Process-Aided Linguistic Explanations），利用流程挖掘技术（如Alpha Miner、iDHM、Inductive Miner）从智能体执行轨迹中提取底层行为流程，并由大语言模型合成人类可读的因果与远端解释。我们在小型跳棋环境中验证了该方法的有效性，为复杂战略领域中混合智能体的可解释性奠定了可扩展基础。
原文摘要 · Abstract (English)
Monte-Carlo Tree Search (MCTS) is a fundamental sampling-based search algorithm widely used for online planning in sequential decision-making domains. Despite its success in driving recent advances in artificial intelligence, understanding the behavior of MCTS agents remains a challenge for both developers and users. This difficulty stems from the complex search trees produced through the simulation of numerous future states and their intricate relationships. A known weakness of standard MCTS is its reliance on highly selective tree construction, which may lead to the omission of crucial moves and a vulnerability to tactical traps. To resolve this, we incorporate shallow, full-width Minimax search into the rollout phase of multi-agent MCTS to enhance strategic depth. Furthermore, to demystify the resulting decision-making logic, we introduce \textsf{M2-PALE} (MCTS--Minimax Process-Aided Linguistic Explanations). This framework employs process mining techniques, specifically the Alpha Miner, iDHM, and Inductive Miner algorithms, to extract underlying behavioral workflows from agent execution traces. These process models are then synthesized by LLMs to generate human-readable causal and distal explanations. We demonstrate the efficacy of our approach in a small-scale checkers environment, establishing a scalable foundation for interpreting hybrid agents in increasingly complex strategic domains.
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