Creating Adjustable Human-like AI Behavior in a 3D Tennis Game with Monte-Carlo Tree Search

Abstract

Interaction with opponents is a core element in video sports games. Thus, user experience in single-player matches heavily depends on the quality of AI opponents, who are expected to vary in their skill level and play styles. One way to achieve this goal is to learn game-playing behavior from real human players and to improve it if necessary with an automated optimization method. Monte-Carlo tree search (MCTS) has been successfully used for this purpose in several card and board games, such as chess and poker. We explore the possibility to apply MCTS in an action sports game of 3D tennis, and show how a dataset of pre-recorded tennis games can be used to train an MCTS-based AI system, exhibiting believable and reasonably skillful behavior.