Cultural learning for Multi-Agent System and its application to fault management

It is usually agreed that a system capable of learning deserves to be called intelligent; and conversely, a system being considered as intelligent is, among other things, usually expected to be able to learn. Learning always has to do with the self-improvement of future behavior based on past experience. In this paper we present a learning model for Multi-Agent System, which aims to the optimization of coordination schemes through a collective learning process based on Cultural Algorithms.