"Multi-Agent systems" is a topic for a lot of research, especially research involving strategy, evolution and cooperation among various agents. Various learning algorithm schemes have been proposed such as reinforcement learning and evolutionary computing.

In this thesis two solutions to a multi-agent herding problem are presented. One solution is based on Q-learning algorithm, while the other is based on modeling of artificial immune system.

Q-learning solution for the herding problem is developed, using region-based local learning for each individual agent. Individual and batch processing reinforcement algorithms are implemented for non-cooperative agents. Agents in this formulation do not share any information or knowledge. Issues such as computational requirements, and convergence are discussed.

An idiotopic artificial immune network is proposed that includes individual B-cell model for agents and T-cell model for controlling the interaction among these agents. Two network models are proposed--one for evolving group behavior/strategy arbitration and the other for individual action selection.

A comparative study of the Q-learning solution and the immune network solution is done on important aspects such as computation requirements, predictability, and convergence.