  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.
