Emergence of Communication for Negotiation by a Recurrent Neural Network

We believe that communication in multi-agent system has two major meanings. One of them is to transmit one agent’s observed information to the other. The other meaning is to transmit what an agent is thinking. Here we focus the latter and aim to the emergence of the autonomous and decentralized arbitration through communication among some agents. The communication contents, strategy and representation are not prescribed and are acquired by learning using a reinforcement signal which is given to the agent after its action. The reinforcement signal is not shared with the other agents. In order to realize this learning, the agent often has to make a decision not only from the present communication signals but also from the past signals. Accordingly the system architecture using recurrent type (Elman) neural network is proposed. The ability of this architecture was examined by two and four agents negotiation problems. A variety of negotiation strategies emerged among the agents through the learning to avoid some conflict after their decisions. 1