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

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.

[1]  A. Barto,et al.  Intrinsic motivations and open-ended development in animals, humans, and robots: an overview , 2014, Front. Psychol..

[2]  A. Maslow.  A Theory of Human Motivation , 1943 .

[3]  Minoru Asada,et al.  What are goals? And if so, how many? , 2015, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

[4]  Pierre-Yves Oudeyer,et al.  Intelligent Adaptive Curiosity: a source of Self-Development , 2004 .

[5]  A. Karmiloff-Smith.  Précis of Beyond modularity: A developmental perspective on cognitive science , 1994, Behavioral and Brain Sciences.

[6]  Richard J. Duro,et al.  Motivational Engine for Cognitive Robotics in Non-static Tasks , 2017, IWINAC.

[7]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.

[8]  Richard J. Duro,et al.  Motivational engine with autonomous sub-goal identification for the Multilevel Darwinist Brain , 2016, BICA 2016.

[9]  Marco Mirolli,et al.  Intrinsically Motivated Learning Systems: An Overview , 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

[10]  Andrés Faiña,et al.  Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots , 2010, IEEE Transactions on Autonomous Mental Development.

[11]  Harlow Hf.  Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. , 1950 .

[12]  Juyang Weng,et al.  Value system development for a robot , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Minoru Asada,et al.  Cognitive developmental robotics as a new paradigm for the design of humanoid robots , 2001, Robotics Auton. Syst..