2010
NIPS
NeurIPS 2010
Repeated Games against Budgeted Adversaries
Abstract
We study repeated zero-sum games against an adversary on a budget. Given that an adversary has some constraint on the sequence of actions that he plays, we consider what ought to be the player's best mixed strategy with knowledge of this budget. We show that, for a general class of normal-form games, the minimax strategy is indeed efficiently computable and relies on a random playout" technique. We give three diverse applications of this algorithmic template: a cost-sensitive "Hedge" setting, a particular problem in Metrical Task Systems, and the design of combinatorial prediction markets."
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization
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Trend Setter
— Game AI
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Keyword Pioneer
— minimax strategy
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Cross-Pollinator
— Artificial Intelligence, Data Science & Analytics, Machine Learning, Mathematics & Optimization, Reinforcement Learning
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Hot Topic Early Bird
— adversarial learning
Authors
Topics
Artificial Intelligence > Core AI > Game AI
Machine Learning > Learning Types > Adversarial Learning
Mathematics & Optimization > Optimization > Online Algorithms
Machine Learning > Optimization & Theory > Online Algorithms
Mathematics & Optimization > Optimization > Game Theory
Artificial Intelligence > Core AI > Game Theory