Quantifying Feature Importance of Games and Strategies via Shapley Values
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866929272808013824 |
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| author | Fujii, Satoru |
| author_facet | Fujii, Satoru |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_11991 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantifying Feature Importance of Games and Strategies via Shapley Values Fujii, Satoru Multiagent Systems 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. |
| title | Quantifying Feature Importance of Games and Strategies via Shapley Values |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2309.11991 |