The article discusses strategies for adapting to behaviorally-biased opponents, including the Follow-the-Leader approach, which bases moves on past payoffs. It emphasizes the use of an ellipsoid algorithm for efficient predictions of opponents' actions while engaging in game play. Variants of strategies, such as limited-history adaptations, allow for polynomial loss bounds. The research suggests a focus on learning optimal responses to enhance competitive performance against various biased strategies, laying groundwork for future explorations in behavioral game theory.
The Follow-the-Leader opponent strategy plays the best historical action to maximize payoff, prompting us to adapt and predict their moves using an ellipsoid algorithm.
Our strategy against a Follow-the-Leader opponent focuses on learning responses to their actions, utilizing an efficient ellipsoid algorithm for action prediction.
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