Thesis (M.S.)--Kent State University, 2007. / Title from PDF t.p. (viewed Mar. 19, 2009). Advisor: Michael Rothstein. Keywords: intrusion detection systems, immune system, immune detectors, intrusion detection squad, multi-agent system. Includes bibliographical references (p. 62-66).

Nature and biological systems have provided the basis for many computational models and systems, such as neural computing and evolutionary computation. This thesis examines the vertebrate immune system which formed the original basis for Artificial Immune Systems (AIS). The vertebrate immune system is highly complex and, for the most part, is successful at providing us with protection from harmful stimuli. Such a system is attractive as a model to inspire a computational system as it exhibits many desirable behaviours: adaptability, diversity, robustness, efficiency and multiple layers of detection. There has been an increasing volume of research in the field of artificial immune systems. However, as the field has expanded, immunological analogies have been reduced at the expense of problem specific optimisation. Hence, this thesis takes a different approach and returns to the immune system which initially inspired research in this field. In this thesis a set of key immunological properties based on immune system concepts and mechanisms are formalised in a model for an artificial immune system. This leads to an AIS framework that is more closely aligned with the immune system, and incorporates both innate and adaptive immune concepts. In particular, antigen presenting cells (APCs), major histocompatibility complex (MHC) molecules and T-cells are modelled within the framework. The differential signalling hypothesis is explored as a model for T-cell development, and provides a novel method for T-cell generation within an AIS. Extensive empirical analysis is performed at an individual level to examine the behaviour of the AIS framework components. These results show that the artificial immune system components exhibit similar properties to the real immune components that inspired them. However, the MHC component of the AIS is found to be of limited value within an individual AIS. The AIS framework is subsequently extended to model a population of artificial immune systems. Further empirical analysis is performed at a population level, and MHC is found to improve the adaptability of an evolving population of artificial immune systems within a dynamic environment. Such a model of immune system function is likely to be useful for immunologists, as it could provide a method of examining immune behaviour under various conditions in a cheaper and more rapid manner than in-vivo or in-vitro. Indeed, it may also provide a solution for examining properties that are unable to be tested using these traditional methods. Finally, the results of these empirical findings are discussed in terms of the relevance and applicability of immunological principles with regard to artificial immune systems for real world problems.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

For the successful deployment of task-achieving multi-robot systems (MRS), the interactions must be coordinated among the robots within the MRS and between the robots and the task environment. There have been a number of impressive experimentally demonstrated coordinated MRS. However it is still of a premature stage for real world applications. This dissertation presents an MRS control scheme using Artificial Immune Systems (AIS). This methodology is firmly grounded in the biological sciences and provides robust performance for the intertwined entities involved in any task-achieving MRS. Based on its formal foundation, it provides a platform to characterize interesting relationships and dependencies among MRS task requirements, individual robot control, capabilities, and the resulting task performance. The work presented in this dissertation is a first of its kind wherein the principles of AIS have been used to model and organize the group behavior of the MRS. This has been presented in the form of a novel algorithm. In addition to the above, generic environments for computer simulation and real experiment have been realized to demonstrate the working of an MRS. These could potentially be used as a test bed to implement other algorithms onto the MRS. The experiment in this research is a bomb disposal task which involves a team of three heterogeneous robots with different sensors and actuators. And the algorithm has been tested practically through computer simulations.