AAAI Publications, Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence

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A Human-Inspired Cognitive Architecture Supporting Self Regulated Learning in Problem Solving
Alexei V. Samsonovich

Last modified: 2010-07-07


Many approaches were explored in recent years to introduce principles of metacognition and meta-learning into cognitive architectures, yet none of them resulted in a scalable human-like learner. This work presents an approach intended to fill the gap between human self-regulated learners and artificial learners by introducing a new spin of the familiar core cognitive architecture paradigm, taking it to a meta-level. The resultant architecture enables in artifacts exclusively human higher cognitive and learning abilities: specifically, deliberative new knowledge construction. Model predictions agree with results of a pilot study with human subjects.


meta-learning; human-level learning; metacognitive architecture

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