Survey on Solutions and Applications for Mixed-motive Games
In recent years,there has been rapid advancement in the application of artificial intelligence technology to sequential decision-making and adversarial game scenarios,resulting in significant progress in domains such as Go,games,poker,and Mahjong.Notably,systems like AlphaGo,OpenAI Five,AlphaStar,DeepStack,Libratus,Pluribus,and Suphx have achieved or surpassed human expert-level performance in these areas.While these applications primarily focus on zero-sum games involving two players,two teams,or multiple players,there has been limited substantive progress in addressing mixed-motive games.Unlike zero-sum games,mixed-motive games necessitate comprehensive consideration of individual returns,collective returns,and equilibrium.These games are extensively applied in real-world applications such as public resource allocation,task scheduling,and autonomous driving,making research in this area crucial.This study offers a comprehensive overview of key concepts and relevant research in the field of mixed-motive games,providingan in-depth analysis of current trends and future directions both domestically and internationally.Specifically,this study first introduces the definition and classification of mixed-motive games.It then elaborates on game solution concepts and objectives,including Nash equilibrium,correlated equilibrium,and Pareto optimality,as well as objectives related to maximizing individual and collective gains,while considering fairness.Furthermore,the study engages in a thorough exploration and analysis of game theory methods,reinforcement learning methods,and their combination based on different solution objectives.In addition,the study discusses relevant application scenarios and experimental simulation environments before concluding with a summary and outlook on future research directions.
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