Quantifying Feature Importance of Games and Strategies via Shapley Values
arXiv:2309.11991 · doi:10.1007/978-3-031-54968-7_8
Abstract
Recent advances in game informatics have enabled us to find strong strategies across a diverse range of games. However, these strategies are usually difficult for humans to interpret. On the other hand, research in Explainable Artificial Intelligence (XAI) has seen a notable surge in scholarly activity. Interpreting strong or near-optimal strategies or the game itself can provide valuable insights. In this paper, we propose two methods to quantify the feature importance using Shapley values: one for the game itself and another for individual AIs. We empirically show that our proposed methods yield intuitive explanations that resonate with and augment human understanding.
arXiv commentAppearing on The Advances in Computer Games conference (ACG 2023)