A novel information measure for predictive learning in a social system setting

Authors:
Paolo Di Prodi;Bernd Porr;Florentin Wörgötter
Affiliations:
University of Glasgow;University of Glasgow;BCCN Göttingen, Germany
Venue:
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Year:
2010

Citing 6
Cited 0

Estimating the errors on measured entropy and mutual information

Physica D
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Strongly improved stability and faster convergence of temporal sequence learning by using input correlations only

Neural Computation
Strongly improved stability and faster convergence of temporal sequence learning by using input correlations only

Neural Computation
Adaptive Communication Promotes Sub-system Formation in a Multi Agent System with Limited Resources

LAB-RS '08 Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
Novel leaning feed-forward controller for accurate robot trajectory tracking

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a new theoretical framework, based on Shannon's communication theory and on Ashby's law of requisite variety, suitable for artificial agents using predictive learning. The framework quantifies the performance constraints of a predictive adaptive controller as a function of its learning stage. In addition, we formulate a practical measure, based on information flow, that can be applied to adaptive controllers which use hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The framework is also useful in quantifying the social division of tasks in a social group of honest, cooperative food foraging, communicating agents. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agents perspective.