Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.

翻译：计算元认知是综合人工系统中高阶推理的认知系统视角,寻求利用人类元认知和人造智能中变相方法的观念,关键特征是在智能系统中宣示并监测认知活动的痕迹,以便管理认知本身的性能。认知的改进随后导致行为上的改善,从而导致绩效的改善。我们用一个名为MIDCA的认知结构的代理实施来说明这些概念,并展示了在解决问题过程中变相的价值。结果说明了计算元认知如何通过元目标操作和学习改变认知来改善绩效。