A hybrid approach for the fighting game ai challenge: balancing case analysis and monte carlo tree search for the ultimate performance in unknown environment

Abstract

The challenging nature of the Fighting Game AI Challenge originates from the short instant of response time which is a typical requirement in real-time fighting games. Handling such real-time constraint requires either tremendous computing power or a clever algorithm design. The former is uncontrollable by the participants, as for the latter, the competition has received a variety of submissions, ranging from the naivest case analysis approach to those using highly advanced computing techniques such as Genetic Algorithms (GA), Reinforcement Learning (RL) or Monte Carlo Tree Search (MCTS), but none could provide a stable solution, especially in the LUD division, where the environment setting is unknown in advance. Our study presents our submission to this challenge in which we designed a winning solution in the LUD division which, for the first time, stably outperformed all players in all competition categories. Our results demonstrate that a proper blend of case analysis and advanced algorithms could result in an ultimate performance.