Learning of mediation strategies for heterogeneous agents cooperation

Making heterogeneous agents cooperate is still an open problem. We have studied the interaction between a human agent and an information service agent. Our approach is to introduce a mediator agent to formalize the requests of the users, according to their profile and then to give the relevant answers. The mediator must find the best mediation strategy (a sequence of interactions) with a Markov decision process (MDP). The states are built on an attribute based referential and the capacity of the source to answer the request under formalization. The actions allow to ask questions to the user or to probe the information source. The rewards reflect the satisfaction of the user, the length of the mediation and the quantity of results. Our prototype uses reinforcement learning (Q-learning) for an on-line adaptation without requiring an a priori model. We describe our experiments on a flight information service with a simulated behaviour.