Solving Imperfect-Information Games via Discounted Regret Minimization
نویسندگان
چکیده
منابع مشابه
Regret Minimization in Games with Incomplete Information
Extensive games are a powerful model of multiagent decision-making scenarioswith incomplete information. Finding a Nash equilibrium for very large instancesof these games has received a great deal of recent attention. In this paper, wedescribe a new technique for solving large games based on regret minimization.In particular, we introduce the notion of counterfactual regret, whi...
متن کاملRegret Minimization in Games with Incomplete Information
Extensive games are a powerful model of multiagent decision-making scenarioswith incomplete information. Finding a Nash equilibrium for very large instancesof these games has received a great deal of recent attention. In this paper, wedescribe a new technique for solving large games based on regret minimization.In particular, we introduce the notion of counterfactual regret, whi...
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Online search in games has always been a core interest of artificial intelligence. Advances made in search for perfect information games (such as Chess, Checkers, Go, and Backgammon) have led to AI capable of defeating the world’s top human experts. Search in imperfect information games (such as Poker, Bridge, and Skat) is significantly more challenging due to the complexities introduced by hid...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33011829