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