Unsupervised training of multiobjective agent communication using genetic programming

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. The problem is further complicated in a multiobjective scenario. In order to solve this problem, in our previous research we had proposed a method applying genetic programming techniques, in particular automatically defined function genetic programming (ADF-GP), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine multiobjective genetic programming in order to adapt the system to a multiobjective environment. In the proposed method separate agent communication protocols are trained for each objective. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method in multiobjective environments