Causal cognitive architecture 1: Integration of connectionist elements into a navigation-based framework

• Published in: Cognitive systems research Vol. 66; pp. 67 - 81
• Main Author: Schneider, Howard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2021
• Subjects: Artificial general intelligence; Causality; Cognitive architecture; Explainability; Spatial navigation; Cognitive architecture; Spatial navigation; Causality; Artificial general intelligence; Explainability
• ISSN: 1389-0417; 1389-0417
• DOI: 10.1016/j.cogsys.2020.10.021

The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Causality emerges not from a central controlling stored program but directly from the architecture. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map, with a resultant navigation signal obtained. Navigation can similarly be for the physical world as well as for a landscape of higher cognitive concepts. There is good explainability for causal decisions. A simulation of the CCA1 controlling a search and rescue robot is presented with the goal of finding and rescuing a lost hiker within a grid world. A simulation of the CCA1 controlling a repair robot is presented that can predict the movement of a series of gears.