Learning and coordination: An overview

Abstract

Adaptive learning techniques can automate the large-scale coordination of multi-agent systems and enhance their robustness in dynamic environments. This paper surveys several learning approaches that have been developed to address three different aspects of coordination, namely, learning coordination behavior, team learning, and the integrated learning of trust and reputation in order to facilitate coordination in open systems including collaborative systems where artificial agents and humans interact. Although convergence in multi-agent learning is still an open research question, several applications have emerged using some of the learning techniques presented.