A Cognitive Control Architecture for an Artificial Creature using Episodic Memory

Abstract

This paper describes a new cognitive control architecture incorporating episodic memory for the artificial creature Rity, the software robot component of the Ubibot, the ubiquitous robot system. The episodic memory is defined as a scalable structure that stores episodic perceptual snapshots as Rity's experience grows. The system also utilizes a temporally variant spatial map to store spatial information and a higher level procedural memory using finite state machines. The system is designed to enable Rity to be cognitive in its approach to task selection through the dual process of experiential and spatial learning. This is brought about through a multi-agent strategy based on six principal modules: perception module, internal state module, behavior selection module, interactive learning module, memory module, and motor module, to control its behavior considering its internal state. Experiments on completion of task and maintenance of ideal internal state are described. The results show that the artificial creature possesses the ability to improve its performance as its experience grows