Artificial immune systems and particle swarm optimization for solutions to the general adversarial agents problem

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either "friendly" or "enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next "timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with related theoretical and analytic issues. A non-linear integer programming formulation of the problem is provided, several novel approaches are explored and compared, and two original algorithms are presented and demonstrated to be more useful than established algorithms for certain classes of problems. As part of the research effort, a software system to solve instances of the general problem is presented, centered on a novel hybrid Artificial Immune Systems / Particle Swarm Optimization algorithm.