Evolving Behaviour Trees for the Commercial Game DEFCON
Evolving Behaviour Trees for the Commercial Game DEFCON
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DOI：
10.1007/978-3-642-12239-2_11
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
Chong-U Lim;Robin Baumgarten;S. Colton
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文献类型：
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作者：
Chong-U Lim;Robin Baumgarten;S. Colton
行为树通过简单的执行，可扩展，能够处理游戏的复杂性以及模块化来提高可重用性，从而为游戏中现有的人工智能技术提供了改进的可能性。这最终改善了设计自动游戏玩家的开发过程。我们在这里介绍使用行为树来设计和开发商业实时战略游戏DEFCON的ai控制玩家。特别是，我们进化了行为树去创造一个具有竞争力的玩家，即能够在超过50%的时间内超越游戏最初的AI-bot。我们的目标是强调进化行为树作为开发游戏中ai机器人的实用方法的潜力。
Behaviour trees provide the possibility of improving on existing Artificial Intelligence techniques in games by being simple to implement, scalable, able to handle the complexity of games, and modular to improve reusability. This ultimately improves the development process for designing automated game players. We cover here the use of behaviour trees to design and develop an AI-controlled player for the commercial real-time strategy game DEFCON. In particular, we evolved behaviour trees to develop a competitive player which was able to outperform the game’s original AI-bot more than 50% of the time. We aim to highlight the potential for evolving behaviour trees as a practical approach to developing AI-bots in games.