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Monte Carlo Tree Search (MCTS) has been extended to many imperfectinformation games. However, due to the added complexity that uncertaintyintroduces, these adaptations have not reached the same level of practicalsuccess as their perfect information counterparts. In this paper we considerthe development of agents that perform well against humans in imperfectinformation games with partially observable actions. We introduce theSemi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTSalgorithm (ISMCTS). More specifically, SDMCTS generates a predictive model ofthe unobservable portion of the opponent's actions from historical behavioraldata. Next, SDMCTS performs simulations on an instance of the game where theunobservable portion of the opponent's actions are determined. Thereby, itfacilitates the use of the predictive model in order to decrease uncertainty.We present an implementation of the SDMCTS applied to the Cheat Game, awell-known card game, with partially observable (and often deceptive) actions.Results from experiments with 120 subjects playing a head-to-head Cheat Gameagainst our SDMCTS agents suggest that SDMCTS performs well against humans, andits performance improves as the predictive model's accuracy increases.

Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games