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Results for "Probability Distributions on a Circle"

Subject: Artificial Intelligence Departments: Department of Computing Science

  • Improving Determinized Search with Supervised Learning in Trick-Taking Card Games

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    Spring 2019

    Solinas, Christopher

    predict which cards opponents are holding based on the cards that have been played so far. Inference is crucial for the performance of algorithms that use determinization because it allows states to be sampled according to a better estimate of the true state probability distribution in the information set

    handling the larger input feature spaces associated with a richer state representation, and lastly, I explain how to combine these predictions to estimate the probability distribution of states within an information set and improve determinized search techniques — leading to a new state-of-the-art in

    imperfect information games. However, these works have largely neglected another important part of the equation: inference. Inference involves estimating the state probability distribution of an information set using state information like past opponent actions. It lets players of trick-taking card games

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