Opponent Modeling in Multiplayer Imperfect-Information Games
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866913449345286144 |
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| author | Ganzfried, Sam Wang, Kevin A. Chiswick, Max |
| author_facet | Ganzfried, Sam Wang, Kevin A. Chiswick, Max |
| contents | In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_06027 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Opponent Modeling in Multiplayer Imperfect-Information Games Ganzfried, Sam Wang, Kevin A. Chiswick, Max Computer Science and Game Theory Artificial Intelligence Multiagent Systems Theoretical Economics In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies. |
| title | Opponent Modeling in Multiplayer Imperfect-Information Games |
| topic | Computer Science and Game Theory Artificial Intelligence Multiagent Systems Theoretical Economics |
| url | https://arxiv.org/abs/2212.06027 |