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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. 

Humans and autonomous agents often have differing knowledge about the world, the goals they pursue, and the actions they perform. Given these differences, an autonomous agent should be capable of rebelling against a goal when its completion would violate that agent’s preferences and motivations. Prior work on agent rebellion has examined agents that can reject actions leading to harmful consequences. Here we elaborate on a specific justification for rebellion in terms of violated goal expectations. Further, the need for rebellion is not always known in advance. So to rebel correctly and justifiably in response to unforeseen circumstances, an autonomous agent must be able to learn the reasons behind violations of its expectations. This paper provides a novel framework for rebellion within a metacognitive architecture using goal monitoring and model learning, and it includes experimental results showing the efficacy of such rebellion.