Mullet's Gambit: Explaining Learned Strategies in the Chef's Hat Multiplayer Card Game

Reinforcement learning (RL)-based agents have demonstrated remarkable performance in multiplayer card game environments such as Chef's Hat. However, understanding why these agents excel in such dynamic and competitive settings remains a challenging endeavor. In this paper, we propose a novel me...

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Bibliographic Details
Published in2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 136 - 143
Main Authors Triglia, Laura, Barros, Pablo, Rea, Francesco, Sciutti, Alessandra
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.09.2024
Subjects
Computational modeling
Conferences
Decision making
Distance measurement
Explainable AI
Explainable game strategies
Gameplay analysis
Games
Heuristic algorithms
Humanagent interaction
Model Explainability
Navigation
Reinforcement learning
Strategic Interactions
Visualization
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Summary:Reinforcement learning (RL)-based agents have demonstrated remarkable performance in multiplayer card game environments such as Chef's Hat. However, understanding why these agents excel in such dynamic and competitive settings remains a challenging endeavor. In this paper, we propose a novel method, named the "Mullet's Gambit" to elucidate the strategies employed by RL-based agents within the context of the Chef's Hat card game. This method aims to provide insights into how RL-based agents navigate the complexities of multiplayer dynamics and assess their impact on opponents. By employing Mullet's Gambit, this investigation reveals the unique traits and efficacy of RL-based strategies compared to heuristic methodologies. This leads to the inference that RL-based agents not only acquire the skills to win but also to disrupt their opponents, thereby minimizing their potential actions.
DOI:10.1109/ACIIW63320.2024.00028