EMMA: Evolving and Messaging Decision Making Agents

We propose to develop a system of mobile, autonomous, evolving agents for collaborative decision making, called EMMA. EMMA will be a neuro-genetic hybrid multi-agent collaborative decision making system based on an evolutionary control strategy for coordinating decision making agents. The system architecture will be two-layers, consisting of a central control agent and a distributed set of mobile collaborating decision making agents. The control strategy will use a neuro-genetic hybrid (NGH) approach to training and evolving autonomous decision making agents. The system will find information, evaluate it, and combine multiple results to offer recommendations and alternatives for action. Phase I will deliver a feasibility study and working prototype with one agent. It will prove the concept and establish the necessary architecture. Phase II will add multiple and mobile agents, and create a working collaborative multi-agent system. Design issues for EMMA include: mechanisms for inter-agent communication and coordination of problem solving; optimum implementations for a polling mechanism and for the genetic algorithm including selection mechanism and reproduction process and development of agent fitness measures; and incorporation of feedback from the user into the learning process.