Exploring Explainable Multi-agent MCTS-minimax Hybrids in Board Game Using Process Mining
This work addresses the explainability and tactical robustness of MCTS for developers and users in board game AI, but it appears incremental as it combines existing techniques.
The paper tackled the problem of explaining MCTS agent behavior and improving its tactical weaknesses by integrating shallow minimax search into multi-agent MCTS rollouts and using process mining for explanation in 3v3 checkers, resulting in a hybrid approach that addresses selective tree issues.
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.