Implementation and Evaluation of Information Set Monte Carlo Tree Search for Pokémon

Abstract

Artificial intelligence has shown remarkable performance in perfect information games. However, it is still no match for human players when it comes to most imperfect information games. Information set Monte Carlo tree search (ISMCTS) has been developed to reduce the effects of strategy fusion caused by determinization of the imperfect information and demonstrated advantages over the conventional Monte Carlo tree search (MCTS) that uses determinization. Because ISMCTS has only been used for games with relatively simple structure, it is still unknown whether it works effectively for more complex games. In this study, we take Pokemon as an example of a complex imperfect information game and implement a simulator to evaluate the effectiveness of ISMCTS. Experimental results show that ISMCTS outperforms the conventional MCTS that uses determinization.