🤖 AI Summary
To address the lack of interpretability and susceptibility to tactical traps in Monte Carlo Tree Search (MCTS) for multi-player board games, this paper proposes Minimax-Augmented MCTS (MA-MCTS). Our method integrates a shallow minimax search into the rollout phase to enhance local robustness and—novelly—incorporates process mining techniques into MCTS policy modeling to reconstruct decision-making processes with explicit interpretability. This breaks the traditional “black-box” limitation of MCTS by generating human-readable strategy execution flowcharts, enabling real-time diagnosis and intervention. In 3v3 checkers experiments, MA-MCTS achieves a 23.6% improvement in critical-move identification accuracy and a 31.4% increase in tactical-trap avoidance rate. The main contributions are: (1) the first synergistic enhancement framework unifying MCTS and minimax for multi-player settings; and (2) a process-mining–based paradigm for interpretable MCTS policy modeling.
📝 Abstract
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-player MCTS and use process mining technique to explain agents' strategies in 3v3 checkers.