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

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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: http://dx.doi.org/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

Publication date: 2009-06-01

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