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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Learning to win in a first-person shooter game
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Learning to win in a first-person shooter game

机译：学习在第一人称射击游戏中获胜

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Designing a character's behavior in a first-person shooter (FPS) game typically requires a considerable amount of effort due to a complex in-game environment. This paper proposes an efficient strategy of training a character in the Quake III Arena, a FPS game, to be more adaptive and also to have human-like learning capabilities and behaviors. Specifically, a particle swarm optimization (PSO) algorithm is introduced to provide a character with selflearning abilities. Like many computer games, the Quake III Arena utilizes a rule-based system which is constrained by several weights in the software. The effectiveness of these constrained weights is dependent upon the programmer's knowledge about the game. In order to increase a character's learning ability, an efficient method using the PSO algorithm is developed to determine the constrained weights for the behavior control. In the conducted experiments, the PSO algorithm is implemented to design a non-player character which is shown to be superior to the other characters originally created in the Quake III game. This efficient training strategy decreases the amount of effort required by game designers to design an intelligent character's behavior.
机译：由于复杂的游戏环境，在第一人称射击游戏（FPS）中设计角色的行为通常需要付出大量努力。本文提出了一种在FPS游戏《雷神之锤》 III竞技场中训练角色的有效策略，使其更具适应性并具有类似于人的学习能力和行为。具体而言，引入了粒子群优化（PSO）算法以提供具有自学习能力的角色。与许多计算机游戏一样，Quake III Arena使用基于规则的系统，该系统受软件中的多个权重约束。这些约束权重的有效性取决于程序员对游戏的了解。为了提高角色的学习能力，开发了一种使用PSO算法的有效方法来确定行为控制的约束权重。在进行的实验中，实施了PSO算法以设计一个非玩家角色，该角色表现出比Quake III游戏中最初创建的其他角色更好的角色。这种有效的培训策略减少了游戏设计师设计智能角色的行为所需的精力。

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