Towards Multi-agent Learning of Causal Networks

We propose a multi-agent protocol for distributed learning of causal networks, aimed both at (i) reducing the complexity of learning large causal networks and (ii) letting agents in a MAS cooperate to unveil causal relationships that individuals could not reveal by themselves, due to partial observability.