Task Allocation in Distributed Artificial Intelligence Using Boids Model

Decentralized multi-agent approach is a promising research field particularly in the area of performance improvement by handling task allocation and communication time. Some recent research has focused on developing the learning process to be better suited for specific problems; other efforts had proven that a generalized solution is better off especially when there is no global controller. This paper presents a better suited multi-membered evolution strategy to agent reasoning with an improved method of pre-assigning initial values to agents.