Exchanging advice and learning to trust

Abstract

One of the most important features of "intelligent behaviour" is the ability to learn from experience. The introduction of Multiagent Systems brings new challenges to the research in Machine Learning. New difficulties, but also new advantages, appear when learning takes place in an environment in which agents can communicate and cooperate. The main question that drives this work is "How can agents benefit from communication with their peers during the learning process to improve their individual and global performances?" We are particularly interested in environments where speed and band-width limitations do not allow highly structured communication, and where learning agents may use different algorithms. The concept of advice-exchange, which started out as mixture of reinforced and supervised learning procedures, is developing into a meta-learning architecture that allows learning agents to improve their learning skills by exchanging information with their peers. This paper reports the latest experiments and results in this subject.