Re-determinizing Information Set Monte Carlo Tree Search in Hanabi
This work addresses a specific issue in cooperative game AI for Hanabi, representing an incremental improvement over existing techniques.
The paper tackled the problem of hidden information leakage in Information Set Monte Carlo Tree Search (IS-MCTS) for the game Hanabi by introducing Re-determinizing IS-MCTS, which scored higher for 2-4 players than previous methods under a 40ms time limit per move.
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.