An evidential cooperative multi-agent system

The cooperative learning systems (COLS) are an interesting way of research in Artificial Intelligence. This is because an intelligence form can emerge by interacting simple agents in these systems. In literature, we can find many learning techniques, which can be improved by combining them to a cooperative learning, this one can be considered as a special case of bagging. In particular, learning classifier systems (LCS) are adapted to cooperative learning systems because LCS manipulate rules and, hence, knowledge exchange between agents is facilitated. However, a COLS has to use a combination mechanism in order to aggregate information exchanged between agents, this combination mechanism must take in consideration the nature of information manipulated by the agents. In this paper we investigate a cooperative learning system based on the Evidential Classifier System, the cooperative system uses Dempster-Shafer theory as a support to make data fusion. This is due to the fact that the Evidential Classifier System is itself based on this theory. We present some ways to make cooperation by using this architecture and discuss the characteristics of such architecture by comparing the obtained results with those obtained by an individual approach.