  Advances in intelligent game playing agents have led to successes in perfect
information games like Go and imperfect information games like Poker. The
Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms
outperforms previous algorithms using Monte Carlo methods in imperfect
information games. In this paper, Single Observer Information Set Monte Carlo
Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction
board game that combines traditional hidden role mechanics with the randomness
of a card deck. This combination leads to a more complex information model than
the hidden role and card deck mechanics alone. It is shown in 10108 simulated
games that SO-ISMCTS plays as well as simpler rule based agents, and
demonstrates the potential of ISMCTS algorithms in complicated information set
domains.
