Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents

• URL: http://arxiv.org/abs/2506.14246v1
• Date: Tue, 17 Jun 2025 07:07:13 GMT
• Title: Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
• Authors: Lingfeng Li, Yunlong Lu, Yongyi Wang, Qifan Zheng, Wenxin Li,
• Abstract summary: This paper introduces Mxplainer, a parameterized search algorithm that can be converted into an equivalent neural network to learn the parameters of black-box agents.<n>Experiments conducted on AI and human player data demonstrate that the learned parameters provide human-understandable insights into these agents' characteristics and play styles.
• Score: 0.8088999193162028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring effective long-term decision-making amidst randomness and hidden information. Through the efforts of AI researchers, several impressive Mahjong AI agents have already achieved performance levels comparable to those of professional human players; however, these agents are often treated as black boxes from which few insights can be gleaned. This paper introduces Mxplainer, a parameterized search algorithm that can be converted into an equivalent neural network to learn the parameters of black-box agents. Experiments conducted on AI and human player data demonstrate that the learned parameters provide human-understandable insights into these agents' characteristics and play styles. In addition to analyzing the learned parameters, we also showcase how our search-based framework can locally explain the decision-making processes of black-box agents for most Mahjong game states.

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