The article explores strategies for engaging with behaviorally-biased opponents in competitive environments. By using an ellipsoid algorithm, the authors suggest that players can predict opponents' actions based on their historical payoffs. Different models such as Myopic Best Responder and Gambler's Fallacy are discussed, illustrating varied behavioral responses of opponents. The focus is on adapting to these biases to exploit weaknesses, while also considering future research directions. The work emphasizes learning and strategic adjustment to optimize performance in uncertain and dynamic scenarios.
In our approach, we propose utilizing an ellipsoid algorithm to effectively predict an opponent's actions while adapting our strategies in challenging environments with behaviorally-biased opponents, ensuring optimal responses.
Exploitations of various behaviorally-biased strategies reveal critical insights into how opponents adapt their actions, emphasizing the need for learning algorithms that efficiently determine the best responses in real-time situations.
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