Learning via gradient descent in Sigma.
Published in Proceedings of the 12th International Conference on Cognitive Modeling, 2013
Recommended citation: Rosenbloom, P. S., Demski, A., Han, T., & Ustun, V. (2013). Learning via gradient descent in Sigma. In Proceedings of the 12th International Conference on Cognitive Modeling (Vol. 94).
Integrating a gradient-descent learning mechanism at the core of the graphical models upon which the Sigma cognitive architecture/system is built yields learning behaviors that span important forms of both procedural learning (e.g., action and reinforcement learning) and declarative learning (e.g., supervised and unsupervised concept formation), plus several additional forms of learning (e.g., distribution tracking and map learning) relevant to cognitive systems/modeling. The core result presented here is this breadth of cognitive learning behaviors that is producible in this uniform manner..