Using Cognitive Architectures to Improve Robot Control: Integrating Production Systems, Semantic Networks, and Sub-Symbolic Processing

In the last several years, a collection of systems have been designed around cognitive psychological principles in an attempt to reproduce the thought patterns of the human mind. These cognitive architectures have made progress in modeling human thought through production system architectures; however, they traditionally have little interaction with the outside world. Additionally, these systems place a heavy burden on the modeler to hand code large memory structures. In this paper, we explore the architecture and capabilities of the Symbolic and Sub-symbolic Robotic Intelligence Control System (SS-RICS) which was inspired by cognitive architectures capable of higher order cognition such as the Adaptive Control of Thought-Rational. SS-RICS integrates production systems, semantic networks, machine learning and sub-symbolic processing to perform real-time mobile robot control. It is our belief that this integration of cognitive psychological techniques and AI techniques will allow mobile robots to have higher order understanding and interaction with the dynamic world in which we live.