Improving Air-to-Air Combat Behavior through Transparent Machine Learning
Improving Air-to-Air Combat Behavior through Transparent Machine Learning
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发表时间：
2014
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通讯作者：
A. Toubman;J. Roessingh;P. Spronck;A. Plaat;H. J. Herik
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作者：
A. Toubman;J. Roessingh;P. Spronck;A. Plaat;H. J. Herik
训练模拟，特别是那些战术训练，需要在对手角色中正确表现计算机生成的力量（CGFs），以获得有效的训练体验。传统上，此类cgf的行为是通过脚本控制的。使用脚本来控制cgf的行为存在两个主要问题：(1)构建有效的脚本需要专业知识，这是非常昂贵的；(2)成本随着场景中“学习事件”的数量（例如新的对手战术）而进一步增加。机器学习技术可以通过自动生成、评估和改进CGF行为来解决这两个问题。本文描述了动态脚本技术在训练模拟中生成CGF行为的一个应用。动态脚本是一种机器学习技术，它通过将规则库中的规则与预定义的行为规则相结合来搜索有效的脚本。虽然动态脚本最初是为商业视频游戏中的人工智能（AI）开发的，但其计算和功能质量也适用于军事训练模拟。在其他特性中，动态脚本以透明的方式生成行为。此外，动态脚本的学习方法是健壮的：通过在初始规则库中使用领域知识来保证最低程度的有效性。在我们的研究中，我们研究了动态脚本在空对空作战中多机协同行为生成中的应用。多智能体系统中的协调仍然是一个不容忽视的问题。我们通过团队成员之间的沟通实现了明确的团队协调。这种协调方法在空战模拟实验中进行了测试，并与由类似的动态脚本设置组成的基线进行了比较，没有明确的协调。在战斗表现方面，使用明确团队协作的团队比基线效率高出20%。最后，本文将讨论动态脚本在实际环境中的应用。
Training simulations, especially those for tactical training, require properly behaving computer generated forces (CGFs) in the opponent role for an effective training experience. Traditionally, the behavior of such CGFs is controlled through scripts. There are two main problems with the use of scripts for controlling the behavior of CGFs: (1) building an effective script requires expert knowledge, which is costly, and (2) costs further increase with the number of ‘learning events’ in a scenario (e.g. a new opponent tactic). Machine learning techniques may offer a solution to these two problems, by automatically generating, evaluating and improving CGF behavior. In this paper we describe an application of the dynamic scripting technique to the generation of CGF behavior for training simulations. Dynamic scripting is a machine learning technique that searches for effective scripts by combining rules from a rule base with predefined behavior rules. Although dynamic scripting was initially developed for artificial intelligence (AI) in commercial video games, its computational and functional qualities are also desirable in military training simulations. Among other qualities, dynamic scripting generates behavior in a transparent manner. Also, dynamic scripting’s learning method is robust: a minimum level of effectiveness is guaranteed through the use of domain knowledge in the initial rule base. In our research, we investigate the application of dynamic scripting for generating behaviors of multiple cooperating aircraft in air-to-air combat. Coordination in multi-agent systems remains a non-trivial problem. We enabled explicit team coordination through communication between team members. This coordination method was tested in an air combat simulation experiment, and compared against a baseline that consisted of a similar dynamic scripting setup, without explicit coordination. In terms of combat performance, the team using the explicit team coordination was 20% more effective than the baseline. Finally, the paper will discuss the application of dynamic scripting in a practical setting.