An Intelligent Fighting Videogame Opponent Adapting to Behavior Patterns of the User

In standard fighting videogames, users usually prefer playing against other users rather than against machines because opponents controlled by machines are in a rut and users can memorize their behaviors after repetitive plays. On the other hand, human players adapt to each other’s behaviors, which makes fighting videogames interesting. Thus, in this paper, we propose an artificial agent for a fighting videogame that can adapt to its users, allowing users to enjoy the game even when playing alone. In particular, this work focuses on combination attacks, or combos, that give great damage to the opponent. The agent treats combos independently, i.e., it is composed of a subagent for predicting combos the user executes, that for choosing combos the agent executes, and that for controlling the whole agent. Human users evaluated the agent compared to static opponents, and the agent received minimal negative ratings. key words: entertainment computing, adapting agent, pattern matching, reinforcement learning