Abstract
Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Space Response Oracles (PSRO), which holds promise to improve scalability by focusing attention on sufficient subsets of strategies. We first motivate PSRO and provide historical context. We then focus on the strategy exploration problem for PSRO: the challenge of assembling effective subsets of strategies that still represent the original game well with minimum computational cost. We survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research.
Original language | English |
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Title of host publication | Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence |
Editors | Kate Larson |
Publisher | International Joint Conferences on Artifical Intelligence (IJCAI) |
Pages | 7951-7961 |
Number of pages | 11 |
ISBN (Electronic) | 978-1-956792-04-1 |
DOIs | |
Publication status | Published - 2024 |
Event | 33rd International Joint Conference on Artificial Intelligence - International Convention Center Jeju (ICC Jeju), Jeju Island, Korea, Republic of Duration: 3 Aug 2024 → 9 Aug 2024 Conference number: 33 https://ijcai24.org/ |
Conference
Conference | 33rd International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI 2024 |
Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 3/08/24 → 9/08/24 |
Internet address |