منابع مشابه
No-regret Learning in Games
The study of learning dynamics in strategic environments has a long history in economic theory. Many different classes of learning algorithms have been considered in the literature and some have been shown to converge to equilibrium under certain conditions. In this note, I focus on a particular class of learning processes, called no-regret learning. While the no-regret framework was originally...
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We consider average-energy games, where the goal is to minimize the long-run average of the accumulated energy. Decidability of average-energy games with a lower-bound constraint on the energy level (but no upper bound) is an open problem; in particular, there is no known upper bound on the memory that is required for winning strategies. By reducing average-energy games with lower-bounded energ...
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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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Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal welfare. This work provides two main technical results that lift this conclusion to games of incomplete information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows dir...
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The game-theoretic solution concept Iterated Regret Minimization (IRM) was introduced recently by Halpern and Pass. We give the first application of IRM to simultaneous voting games. We study positional scoring rules in detail and give theoretical results demonstrating the bias of IRM toward sincere voting. We present comprehensive simulation results of the effect on social welfare of IRM compa...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i04.5851