EVOLVING A TEAM IN A FIRST-PERSON SHOOTER GAME BY USING A GENETIC ALGORITHM

Abstract

Evolving game agents in a first-person shooter game is important to game developers and players. Choosing a proper set of parameters in a multiplayer game is not a straightforward process because consideration must be given to a large number of parameters, and therefore requires effort and thorough knowledge of the game. Thus, numerous artificial intelligence (AI) techniques are applied in the designing of game characters’ behaviors. This study applied a genetic algorithm to evolve a team in the mode of One Flag CTF in Quake III Arena to behave intelligently. The source code of the team AI is modified, and the progress of the game is represented as a finite state machine. A fitness function is used to evaluate the effect of a team's tactics in certain circumstances during the game. The team as a whole evolves intelligently, and consequently, effective strategies are discovered and applied in various situations. The experimental results have demonstrated that the proposed evolution method is capable of evolving a team's behaviors and optimizing the commands in a shooter game. The evolution strategy enhances the original game AI and assists game designers in tuning the parameters more effectively. In addition, this adaptive capability increases the variety of a game and makes gameplay more interesting and challenging.