IngentaConnect A model for the dynamic coordination of multiple competing goals

Authors: Martin H., Jose Antonio¹; de Lope, Javier²

Source: Journal of Experimental & Theoretical Artificial Intelligence, Volume 21, Number 2, June 2009 , pp. 123-136(14)

Publisher: Taylor and Francis Ltd

Abstract:

A general framework for the problem of coordination of multiple competing goals in dynamic environments for physical agents is presented. This approach to goal coordination is a novel tool to incorporate a deep coordination ability to pure reactive agents. The framework presented is based on the notion of multi-objective optimisation. In this article we propose a kind of 'aggregating functions' formulation with the particularity that the aggregation is weighted by means of a dynamic weighting unitary vector [image omitted], which is dependent from the system dynamic state allowing the agent to dynamically coordinate the priorities of its single goals. This dynamic weighting unitary vector is represented as a (n - 1) set of angles. The dynamic coordination must be established by means of a mapping between the state of the agent's environment S to the set of angles Φi(S) by means of any sort of machine-learning tool. In this work, we investigate the use of Reinforcement Learning as a first approach to learn that mapping.

Keywords: goal coordination; conflicting goals; multi-objective optimisation; reinforcement learning

Document Type: Research article

DOI: 10.1080/09528130802113364

Affiliations: 1: Dep. Sistemas Informaticos y Computacion, Universidad Complutense de Madrid, Madrid, Spain 2: Department of Applied Intelligent Systems, Universidad Politecnica de Madrid, Madrid, Spain

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$45.09 plus tax