Perceptual Generalization and Context in a Network Memory Inspired Long-Term Memory for Artificial Cognition

• DOI: 10.1142/s0129065718500533
• Publication Date: 2018-11-15T04:02:48Z
• Authors: Richard J. Duro; Jose A. Becerra; Juan Monroy; Francisco Bellas
• Keywords: Redes neuronales (Informática); Memory, Long-Term; Artificial Intelligence; 05 social sciences; Memoria; Perception; 0501 psychology and cognitive sciences; Neural Networks, Computer; Robotics; Robótica

In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call “automation” of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure. It also addresses the problem of continuous perceptual spaces and the task- and context-related generalization or categorization of perceptions in an autonomous manner within the embodied sensorimotor apparatus of the robot. These issues are analyzed and a solution is proposed through the introduction of two new types of knowledge nuggets: P-nodes representing perceptual classes and C-nodes representing contexts. The approach is studied and its performance evaluated through its implementation and application to a real robotic experiment.