Intelligence without representation
Artificial Intelligence
Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Learning to Coordinate Multi-robot Competitive Systems by Stimuli Adaptation
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
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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.