In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. While scripted agents offer predictability and consistency in controlled environments, they fall short in dynamic, complex scenarios due to their inherent inflexibility. Conversely, RL agents excel in adaptability and learning, offering potential improvements in handling unforeseen situations, but suffer from significant challenges such as black-box decision-making processes and scalability issues in larger simulation environments. This paper introduces a novel hierarchical hybrid artificial intelligence (AI) approach that synergizes the reliability and predictability of scripted agents with the dynamic, adaptive learning capabilities of RL. By structuring the AI system hierarchically, the proposed approach aims to utilize scripted agents for routine, tactical-level decisions and RL agents for higher-level, strategic decision-making, thus addressing the limitations of each method while leveraging their individual strengths. This integration is shown to significantly improve overall performance, providing a robust, adaptable, and effective solution for developing and training intelligent agents in complex simulation environments.

翻译：在支持兵棋推演的作战模拟领域，智能体的开发长期以来主要采用基于规则的脚本化方法，深度强化学习（RL）方法直到最近才被引入。虽然脚本化智能体在受控环境中具有可预测性和一致性，但由于其固有的不灵活性，在动态、复杂的场景中表现不足。相反，强化学习智能体在适应性和学习能力方面表现出色，在处理突发情况方面具有潜在优势，但也面临重大挑战，例如决策过程的黑箱特性以及在大型模拟环境中的可扩展性问题。本文提出了一种新颖的分层混合人工智能（AI）方法，该方法将脚本化智能体的可靠性和可预测性与强化学习的动态、自适应学习能力协同结合。通过分层构建人工智能系统，所提出的方法旨在利用脚本化智能体处理常规、战术层面的决策，而利用强化学习智能体进行更高层次的战略决策，从而在发挥各自优势的同时，解决每种方法的局限性。研究表明，这种整合显著提高了整体性能，为在复杂模拟环境中开发和训练智能体提供了一个稳健、适应性强且有效的解决方案。