Utility Model Re-description within a Motivational System for Cognitive Robotics

Abstract

This paper describes a re-descriptive approach to the efficient acquisition of ever higher level and more precise utility models within the motivational system (MotivEn) of a cognitive architecture. The approach is based on a two-step process whereby, as a first step, simple imprecise sensor correlation related utility models are obtained from the interaction traces of the robot. These utility models allow the robot to increase the frequency of achieving goals, and thus, provide lots of traces that can be used to try to train precise value functions implemented as artificial neural networks. The approach is tested experimentally on a real robotic setup that involves the coordination of two robots.