Dynamic goal coordination in physical agents

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 is based on the notion of multi-objective optimization. We propose a kind of “aggregating functions” formulation with the particularity that the aggregation is weighted by means of a dynamic weighting unitary vector − →ω (S) which is dependant on 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 set of n−1 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) using any sort of machine learning tool. In this work we investigate the use of Reinforcement Learning as a first approach to learn that mapping.