& 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.

Computer team games have attracted many players in recent years. Most of them are rule-based systems because they are simple and easy to implement. However, they usually cause a game agent to be inflexible, and it may repeat a failure. Some studies investigated the learning of a single game agent, and its learning capability has been improved. However, each agent in a team is independent and it does not cooperate with others in a multiplayer game. This article explores an evolution strategy for a computer team game based on Quake III Arena. The Particle Swarm Optimization (PSO) algorithm will be applied to evolve a non-player character (NPC) team in Quake III to be more efficient and intelligent. The evolution of a single NPC, which accommodates to its team and, moreover, the team has learning and cooperating abilities, will be discussed. An efficient team is composed of various members with their own specialties, and the leader is capable of evaluating the performance of a member and assigning it a proper job. Furthermore, the leader of an intelligent team will adapt a strategy appropriate for various circumstances and obtain the team's best performance. Instead of considering the tactic of an individual bot, this article takes the strategy of a team into account.

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