In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.

翻译：在快速变化的灾害响应环境中，自主智能体的规划与决策涉及复杂且相互依赖的选择。尽管近期进展已改进了传统人工智能方法，但这些方法在此类场景中仍常面临困难，尤其当智能体在超出其明确定义的训练参数范围外运作时。为应对这些挑战，我们提出一种受双过程理论启发的基于注意力的认知架构。该框架以在线方式整合了快速但启发式（类人）的响应（系统1）与机器智能虽缓慢但经过优化的规划能力（系统2）。我们阐释了监督控制器如何通过评估两者在多个不同属性上的表现，实时动态地决定启用任一系统，从而优化任务目标。通过在动态环境中的轨迹规划任务进行评估，我们的框架表明这种协同整合能通过优化多项任务目标来有效管理复杂任务。