  This technical report documents the winner of the Computational Intelligence
in Games(CIG) 2018 Hanabi competition. We introduce Re-determinizing IS-MCTS, a
novel extension of Information Set Monte Carlo Tree Search (IS-MCTS) that
prevents a leakage of hidden information into opponent models that can occur in
IS-MCTS, and is particularly severe in Hanabi. Re-determinizing IS-MCTS scores
higher in Hanabi for 2-4 players than previously published work at the time of
the competition. Given the 40ms competition time limit per move we use a
learned evaluation function to estimate leaf node values and avoid full
simulations during MCTS. For the Mixed track competition, in which the identity
of the other players is unknown, a simple Bayesian opponent model is used that
is updated as each game proceeds.
