A Multi-agent system (MAS) is one which has a number of independent software agents interact with each other to achieve a common goal or goals automatically. It is commonly known that many species are living in the form of a MAS as it has high adaptability on surviving in an unstructural environment. Based on the benefits of group living observed from the nature, MAS is a potential direction to solve a wide range of engineering problems including robotics, chemistry, finance and genetics. / As the goodness of a response is measured from the quantity of award received, optimization is necessary in self-organization. We proposed a novel global optimization algorithm called "Creativity Driven Optimization (CDO)" in the second part of this thesis. By introducing the idea of creativity, CDO requires fewer evaluations than that of three reference methods to search for a global optimum. / In order to maximize the ability of MAS, self-organization is an essential element to be considered. Self-organization refers to a process in which the knowledge of a system accumulated automatically without being guided by an outside source or super-intelligence. In addition, the knowledge is accumulated only when the system interacts with the environment. Therefore, a robust self-organizing MAS should use a minimum number of interaction to construct the strategy for a maximum award. This goal can be achieved by involving the unstructural environment modeling and optimal response generation. / In the first part of this thesis, a radial basis function (RBF) network called "Agent Swarm Regression Network (ASRN)" is proposed in which the training algorithm is modeled as an evolution of a rule-based MAS. Three sets of experiments show that the performance of ASRN is better than that of a conventional approach in terms of computation, complexity and memory usage. The experimental results show the acceptable generalization ability and accuracy of ASRN. / In the last part of this thesis, we presented a procedure learning algorithm of self-organizing agent (PLSOA) that consists of CDO, MRN and RKL. Instead of searching for the current response with a local maximum award with reinforcement learning, PLSOA generates a response sequence by optimizing an adaptive objective function that can adjust iteratively. The experimental results of three benchmark problems show that PLSOA is able to generate nearly optimal-length response sequences in three benchmark environments. In addition, the proposed algorithm has an advantage over the reference methods in terms of reduction on procedure evaluation. / In this thesis, we have made some major contributions towards a generalized self-organizing MAS which try to mimic the MAS in nature. / To tackle the modeling process of an unstructural environment, a sequentially trained neural network called "Memory Re gression Network (MRN)" is proposed in the third part of this thesis. Based on the human's learning strategy, fewer training samples are required to train MRN in which the accuracy and generalization of MRN is similar to a reference network. After estimating the environment, the optimal response function is constructed by the estimated environment with the cooperation of a newly proposed algorithm: Response Knowledge Learning (RKL). The simulation result shows that the predator trained by RKL catches the prey within 15 steps after 50 independent successful hunting trials. / Chow Chi-kin. / "November 2005." / Advisers: H. T. Tsui; J. B. Xu. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6604. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 240-259). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

This dissertation employs the theory of self-organizing criticality (SOC) into the study of Chinese cities. SOC was proposed at the end of the 1980s to explain system complexity by combining both self-organizing and critical behaviors. SOC has been broadly used in explaining phenomena in physical and social sciences. However, few attempts have been made to connect urban studies with SOC because of the extreme complexity of urban phenomena. This study develops a generalized SOC to study Chinese cities at both the inter-urban and the intra-urban levels. At the inter-urban level, this study finds that the rank size distribution of Chinese cities has followed Zipf's law since 1984. In addition, the rank size dynamics of Chinese cities experienced a spatiotemporal shift. Before 1996, city rank increases in a few small- and middle-sized cities because of favorable economic policies offered by the central government. After 1996, a majority of the Chinese cities began to be involved in this rank size shuffling. Cities with increasing ranks present clustered distribution, mainly along the south and east coastal areas. Part of the reason is that the market economy mechanism has transcended policy factors in determining the city competitiveness. At the intra-urban level, the study shows that Shenzhen's urban physical development is currently facing physical environmental thresholds, shifting the development strategies spatiotemporally from fringe and isolated growth to fringe and infill growth. The resulted urban patches show power law relationship both in the area-perimeter distributions and the magnitude-frequency distributions. In summary, this research proves the applicability of the generalized SOC in urban studies. At both the inter-urban and the intra-urban levels, the Chinese cities present the characteristics of SOC. Given a stable condition of power law, shifts occur in the inside dynamics of China's urban system and Shenzhen city. This study is one of the few empirical urban studies based on SOC. The study contributes to the literature on SOC theory and provides theoretical breakthroughs in studying Chinese cities. Finally, this study has potential implications on urban policies and urban development strategies.

Thesis (M.S.) -- Central Connecticut State University, 2009. / Thesis advisor: Daniel Larose. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 37-39). Also available via the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 119-122).

Thesis (Ph.D.)--University of Central Florida, 2009. / Adviser: Thomas T. H. Wan. Includes bibliographical references (p. 196-209).

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

This thesis explores self-organisation in SMEs, and has developed a framework for the role of self-organisation in the handling of adaptive challenge by enterprises, based on existing theory and the empirical findings of this study. Analysing self-organisation in three Australian SMEs, a quantitative measure of the ability to self-organise (ASO index) is proposed, with communication as the main driver of self organisation. The efficacy of the ASO index scores for the SMEs is substantiated by empirical evidence of self-organisation. / Thesis (PhD)--University of South Australia, 2006.

This thesis explores self-organisation in SMEs, and has developed a framework for the role of self-organisation in the handling of adaptive challenge by enterprises, based on existing theory and the empirical findings of this study. Analysing self-organisation in three Australian SMEs, a quantitative measure of the ability to self-organise (ASO index) is proposed, with communication as the main driver of self organisation. The efficacy of the ASO index scores for the SMEs is substantiated by empirical evidence of self-organisation. / Thesis (PhD)--University of South Australia, 2006.

This thesis explores self-organisation in SMEs, and has developed a framework for the role of self-organisation in the handling of adaptive challenge by enterprises, based on existing theory and the empirical findings of this study. Analysing self-organisation in three Australian SMEs, a quantitative measure of the ability to self-organise (ASO index) is proposed, with communication as the main driver of self organisation. The efficacy of the ASO index scores for the SMEs is substantiated by empirical evidence of self-organisation.

Thesis (Ph.D.) -- University of Rhode Island, 2007 / Typescript. Includes bibliographical references (leaves 73-82).