@article {Martin H:June 2009:0952-813X:123,
	author = "Martin H, Jose Antonio",
	author = "de Lope, Javier",
	title = "A model for the dynamic coordination of multiple competing goals",
	journal = "Journal of Experimental & Theoretical Artificial Intelligence",
	volume = "21",
	year = "June 2009",
	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.",
	pages = "123-136(14)",
	url = "http://www.ingentaconnect.com/content/tandf/teta/2009/00000021/00000002/art00002"
	doi = "doi:10.1080/09528130802113364"
}