Toward Human-Like Representation Learning for Cognitive Architectures
DOI:
https://doi.org/10.1609/aaaiss.v3i1.31274Keywords:
Representation Learning, Cognitive Architecture, Perception, Deep Learning, Common Model Of Cognition, Soar, Machine LearningAbstract
Human-like learning includes an ability to learn concepts from a stream of embodiment sensor data. Echoing previous thoughts such as those from Barsalou that cognition and perception share a common representation system, we suggest an addendum to the common model of cognition. This addendum poses a simultaneous semantic memory and perception learning that bypasses working memory, and that uses parallel processing to learn concepts apart from deliberate reasoning. The goal is to provide a general outline for how to extend a class of cognitive architectures to implement a more human-like interface between cognition and embodiment of an agent, where a critical aspect of that interface is that it is dynamic because of learning.Downloads
Published
2024-05-20
Issue
Section
Symposium on Human-Like Learning