Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI

Abstract

This paper evaluates the performance of Monte-Carlo Tree Search (MCTS) in a fighting game AI and proposes an improvement for the algorithm. Most existing fighting game AIs rely on rule bases and react to every situation with predefined actions, making them predictable for human players. We attempt to overcome this weakness by applying MCTS, which can adapt to different circumstances without relying on predefined action patterns or tactics. In this paper, an AI based on Upper Confidence bounds applied to Trees (UCT) and MCTS is first developed. Next, the paper proposes improving the AI with Roulette Selection and a rule base. Through testing and evaluation using FightingICE, an international fighting game AI competition platform, it is proven that the aforementioned MCTS-based AI is effective in a fighting game, and our proposed improvement can further enhance its performance.