Opponent Modeling And Belief Inference

Paper on imperfect information games

Unique: Implements incremental Bayesian belief updating specifically for game contexts, allowing real-time refinement of opponent models as new information arrives, rather than batch retraining approaches used in general ML

vs others: More sample-efficient than pure neural network opponent modeling because it leverages game-theoretic structure and explicit probability distributions, enabling faster adaptation with limited game history