Date of Award

4-2013

Degree Name

Doctor of Philosophy

Department

Computer Science

First Advisor

Dr. Dionysios I. Kountanis

Second Advisor

Dr. Ala Al-Fuqaha

Third Advisor

Dr. Matthew Castanier

Fourth Advisor

Dr. Wuwei Shen

Keywords

artificial intelligence, machine learning, optimization, classification algorithms

Abstract

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.

Access Setting

Dissertation-Open Access

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