Trust among individuals is essential for transactions. A human or software agent in need of resources may reduce transaction risk by modeling the trustworthiness of potential partners. Experience- and reputation-based trust models have unique advantages and disadvantages depending on environment factors, including availability of experience opportunities, trustee trustworthiness dynamics, reputation accuracy, and reputation cost. This research identifies how trusters may utilize both experience- and reputation-based trust modeling to achieve more accurate decision-making tools than using either modeling technique alone. The research produces: 1) the Adaptive Trust Modeling technique for combining experience- vs. reputation-based models to produce the most accurate aggregated model possible, 2) a quantitative analysis of the tradeoffs between experience- and reputation-based models to determine conditions under which each type of model is favorable, and 3) an Adaptive Cost Selection algorithm for assessing the value of trust information given acquisition costs. Experiments show that Adaptive Trust Modeling yields an aggregate trust model more accurate than either experience- or reputation-based modeling alone, and Adaptive Cost Selection acquires the optimal combination of trust information, maximizing a truster's transaction payoff while minimizing trust information costs. These tools enable humans and software agents to make effective trust-based decisions given dynamic system conditions.

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

Thesis (Ph.D.)--University of Melbourne, Dept. of Information Systems, 2005. / Typescript (photocopy). Includes bibliographical references (p. 195-215).

No description available.

M.Sc. / The use of computer systems has changed over the years. Modern computer systems operate in an environment that is open, distributed and heterogeneous. They have the capability of locating information stored in remote locations and satisfying the interests and objectives of different users. However, the increase in user demands and the complexity of computers and information systems has caused research to focus on multi-agent systems as a solution to address these demands and complexities. The dissertation deals with the study of single agents and multi-agent systems. The study focuses on the concepts of agents, agent architecture and multi-agent systems. In addition to the study, a taxonomy for specialised agents is proposed. The taxonomy aims at classifying agent-based systems applied in the industry for addressing specific problems. In order to achieve this, a broad survey on agent-based systems in the industry was conducted. The areas under considerations were the financial, health, agricultural, aviation and the information technology sectors. The following dimensions were used to identify the agents in the specific area: • Which application domain is the multi-agent system designed for, developed and deployed in? • What is the specific task or problem the agents are designed to solve? • Do the agents have core or advanced agent attributes in general? The taxonomy is important because agent-based systems are becoming common in the industry and are suitable to address issues (such as locating distributed information and addressing specific needs of computer system users) of open, distributed and heterogeneous computer environments.

M.Sc. / This dissertation investigates how auctioneer agents can maximise the revenue of an auction. Auctions are an effective solution to agent negotiation because of their simplicity. They are therefore the most widely used approach to agent negotiation. A review of auction theory proves that auction revenue is influenced by factors such as the auction format and the auction parameters. The optimal auction format and parameters are dependent on the bidders and the auction environment. A study of internet auctions confirms the findings of auction theory regarding the importance of the format as well as the parameters in an auction. There has been significant research on the strategies bidding agents can adopt in order to maximise their utility. There is little research, however, on the strategies auctioneer agents can adopt to maximise auction revenue. In this study, a survey is done of the related auctioneer agents in research that maximise auction revenue. The shortcomings of these are identified. The aim of this research is to develop an adaptive auctioneer agent that can maximise the revenue of an auction. The proposed solution is the revenue maximising adaptive auctioneer (RMAA) agent. The RMAA agent consists of three components: the auction manager, the auction format selector (AFS) and the auction parameter selector (APS). The AFS employs a customised zeroth-level classifier system to select the auction format based on the auction environment. The APS employs reinforcement learning combined with a local hill climbing algorithm to learn what the most profitable values are for auction parameters, such as the reserve price and the bid increment. The auction manager automates the auction and coordinates the AFS and APS. It implements the four traditional auction formats, namely the English auction, the Dutch auction, the Vickrey auction and the first-price sealed-bid auction. Experiments conducted to evaluate the effectiveness of the RMAA agent prove that this agent can consistently generate more revenue than a non-adaptive auctioneer agent. The contribution of this research is an auctioneer agent that adapts both the auction format and the auction parameters according to the environment in order to maximise the auction revenue.

Lam Ka Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 90-95). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivations --- p.3 / Chapter 1.2 --- Aims --- p.4 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Thesis Outline --- p.5 / Chapter 2 --- Improved Recursive Modeling Method (RMM) --- p.7 / Chapter 2.1 --- An Illustrative Example --- p.7 / Chapter 2.2 --- Recursive Modeling Method (RMM) --- p.10 / Chapter 2.2.1 --- Payoff Matrices --- p.10 / Chapter 2.2.2 --- Infinite Recursive Hierarchy --- p.12 / Chapter 2.2.3 --- Choosing an Action with RMM --- p.13 / Chapter 2.3 --- Improved RMM --- p.15 / Chapter 2.3.1 --- Infinite Recursive Hierarchy --- p.15 / Chapter 2.3.2 --- The Sub-matrix Operator --- p.15 / Chapter 2.3.3 --- Finite Recursive Hierarchy --- p.17 / Chapter 2.3.4 --- Choosing an Action --- p.19 / Chapter 2.3.5 --- Recursive Formulas --- p.21 / Chapter 2.4 --- Original RMM vs. Improved RMM --- p.25 / Chapter 2.4.1 --- Terminating the Infinite Hierarchy --- p.25 / Chapter 2.4.2 --- Resultant Payoff --- p.26 / Chapter 2.5 --- Summary --- p.27 / Chapter 3 --- A Trust/Honesty Model --- p.28 / Chapter 3.1 --- The Need for a Trust Model --- p.28 / Chapter 3.1.1 --- Motivation to Tell the Truth: Invitation to Cooperate --- p.29 / Chapter 3.1.2 --- Motivation to Tell a Lie: to Prevent Competition --- p.29 / Chapter 3.1.3 --- "To Believe, or Not to Believe, that is the Question" --- p.30 / Chapter 3.2 --- The Trust Model --- p.31 / Chapter 3.2.1 --- Impression --- p.31 / Chapter 3.2.2 --- Reputation --- p.34 / Chapter 3.2.3 --- Risk Attitude and Trustworthiness --- p.34 / Chapter 3.2.4 --- Persuasiveness of a Message vs. Stubbornness of the Receiver --- p.37 / Chapter 3.3 --- The Need for an Honesty Model --- p.42 / Chapter 3.3.1 --- "To Lie, or Not to Lie, that is the Question" --- p.43 / Chapter 3.3.2 --- Problem of Living a Lie --- p.43 / Chapter 3.4 --- The Honesty Model --- p.44 / Chapter 3.4.1 --- Impression --- p.44 / Chapter 3.4.2 --- Reputation --- p.46 / Chapter 3.4.3 --- Risk Attitude and Deceivability --- p.46 / Chapter 3.4.4 --- Temptation of Lying vs. Sincerity of the Sender --- p.47 / Chapter 3.5 --- Duality of the Trust/Honesty Model --- p.52 / Chapter 3.6 --- Performance of the Trust/Honesty Model --- p.52 / Chapter 3.7 --- Summary --- p.56 / Chapter 4 --- Adaptive Strategies --- p.57 / Chapter 4.1 --- Problem of Non-adaptive Agents --- p.57 / Chapter 4.2 --- The Adaptive Strategies --- p.60 / Chapter 4.3 --- Variations of Parameters --- p.61 / Chapter 4.4 --- Adaptive Agents vs. Non-adaptive Agents --- p.63 / Chapter 4.5 --- Summary --- p.67 / Chapter 5 --- Related Work --- p.68 / Chapter 5.1 --- "Impression, Reputation and Trust" --- p.68 / Chapter 5.2 --- Theory of Honesty --- p.72 / Chapter 5.3 --- Summary --- p.72 / Chapter 6 --- Performance Analysis --- p.73 / Chapter 6.1 --- Simulation Settings --- p.73 / Chapter 6.2 --- Performance in Semi-competitive Environment --- p.75 / Chapter 6.2.1 --- Performance of Receivers --- p.75 / Chapter 6.2.2 --- Performance of Senders --- p.78 / Chapter 6.3 --- Performance when Interact with Strategic Senders --- p.80 / Chapter 6.3.1 --- Senders Telling More Truths than Lies --- p.81 / Chapter 6.3.2 --- Senders Telling More Lies than Truths --- p.83 / Chapter 6.4 --- Summary --- p.84 / Chapter 7 --- Conclusion and Future Work --- p.85 / Chapter 7.1 --- Conclusions --- p.85 / Chapter 7.2 --- Future Work --- p.87 / Chapter 7.2.1 --- Agent Manipulation --- p.87 / Chapter 7.2.2 --- Algorithm for Solving Recursive Formulas --- p.88 / Chapter 7.2.3 --- Fuzzy Trust/Honesty Model --- p.88 / Chapter 7.2.4 --- Opinion from the Mass --- p.88 / Chapter 7.2.5 --- Network Application --- p.89 / Bibliography --- p.90

Miu, Hoi Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 170-182). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.4 / Chapter 1.2 --- Contributions --- p.5 / Chapter 1.3 --- Thesis Organization --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Multi-agent Systems --- p.8 / Chapter 2.1.1 --- Agent Architectures --- p.9 / Chapter 2.1.2 --- Multi-agent system frameworks --- p.12 / Chapter 2.1.3 --- The Advantages and Disadvantages of Multi-agent Systems --- p.15 / Chapter 2.2 --- Evolutionary Computation --- p.16 / Chapter 2.2.1 --- Genetic Algorithms --- p.17 / Chapter 2.2.2 --- Genetic Programming --- p.18 / Chapter 2.2.3 --- Evolutionary Strategies --- p.19 / Chapter 2.2.4 --- Evolutionary Programming --- p.19 / Chapter 2.3 --- Object Recognition --- p.19 / Chapter 2.3.1 --- Knowledge Representation --- p.20 / Chapter 2.3.2 --- Object Recognition Methods --- p.21 / Chapter 2.4 --- Evolutionary Multi-agent Systems --- p.25 / Chapter 2.4.1 --- Competitive Coevolutionary Agents --- p.26 / Chapter 2.4.2 --- Cooperative Coevolutionary Agents --- p.26 / Chapter 2.4.3 --- Cellular Automata --- p.27 / Chapter 2.4.4 --- Emergent Behavior --- p.28 / Chapter 2.4.5 --- Evolutionary Agents for Image processing and Pattern Recog- nition --- p.29 / Chapter 3 --- System Architecture and Agent Behaviors in SIMULATORS --- p.33 / Chapter 3.1 --- Organization of the System --- p.34 / Chapter 3.1.1 --- General Architecture of Object Recognition System --- p.34 / Chapter 3.1.2 --- Introduction to SIMULATORS --- p.35 / Chapter 3.1.3 --- System Flow of SIMULATORS --- p.37 / Chapter 3.1.4 --- Layered Digital Image Environment --- p.39 / Chapter 3.2 --- Architecture of Autonomous Agents --- p.41 / Chapter 3.2.1 --- Internal Object Model in an Agent --- p.41 / Chapter 3.2.2 --- Current State of an Agent --- p.46 / Chapter 3.2.3 --- Local Information Sensor --- p.46 / Chapter 3.2.4 --- Direction Density Vector --- p.47 / Chapter 3.3 --- Agent Behaviors --- p.48 / Chapter 3.3.1 --- Feature Target Marking --- p.49 / Chapter 3.3.2 --- Reproduction --- p.49 / Chapter 3.3.3 --- Diffusion --- p.52 / Chapter 3.3.4 --- Vanishing --- p.54 / Chapter 3.4 --- Clustering for Autonomous Agent Training --- p.56 / Chapter 3.4.1 --- Introduction --- p.56 / Chapter 3.4.2 --- Creating the Internal Object Model --- p.58 / Chapter 3.5 --- Summary --- p.63 / Chapter 4 --- Evolutionary Algorithms for Multi Agent System --- p.64 / Chapter 4.1 --- Evolutionary Agent Behaviors in SIMULATORS --- p.65 / Chapter 4.1.1 --- Overview --- p.65 / Chapter 4.1.2 --- Evolutionary Autonomous Agents --- p.66 / Chapter 4.1.3 --- Reproduction --- p.68 / Chapter 4.1.4 --- Fitness Function --- p.68 / Chapter 4.1.5 --- Direction Density Vector Propagation --- p.73 / Chapter 4.1.6 --- Mutation --- p.73 / Chapter 4.2 --- Agents Voting Mechanism --- p.74 / Chapter 4.2.1 --- Overview --- p.74 / Chapter 4.2.2 --- Voting for Cooperative Agents --- p.75 / Chapter 4.3 --- Evolutionary Multi Agent Object Recognition --- p.79 / Chapter 4.4 --- Summary --- p.81 / Chapter 5 --- Experimental Results and Applications --- p.82 / Chapter 5.1 --- Experiment Methodology --- p.82 / Chapter 5.1.1 --- Introduction to Fung Shui Woodland --- p.83 / Chapter 5.1.2 --- Testing Images --- p.83 / Chapter 5.1.3 --- Creating Internal Object Model --- p.85 / Chapter 5.1.4 --- Experiment Parameters --- p.86 / Chapter 5.2 --- Experimental Results of Fung Shui Woodland Recognition --- p.92 / Chapter 5.2.1 --- Experiment 1: artificial0l --- p.92 / Chapter 5.2.2 --- Experiment 2: artificial0l´ؤnoise --- p.92 / Chapter 5.2.3 --- Experiment 3: artificial02 --- p.93 / Chapter 5.2.4 --- Experiment 4: FungShui0l --- p.93 / Chapter 5.2.5 --- Experiment 5: FungShui0l´ؤnoise --- p.94 / Chapter 5.2.6 --- Experiments 6 to 11: FungShui02 to FungShui07 --- p.94 / Chapter 5.3 --- Discussion --- p.119 / Chapter 5.4 --- An Example of Eyes Detection --- p.124 / Chapter 5.4.1 --- Result of the Eyes Detection --- p.128 / Chapter 5.5 --- Summary --- p.132 / Chapter 6 --- Conclusion --- p.133 / Chapter 6.1 --- Summary --- p.133 / Chapter 6.2 --- Future Work --- p.136 / Chapter A --- The Figures in the Experiments --- p.138

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.

近年来，由于多智能体系统广泛应用于分布式传感器网络的协调和控制、移动机器人、自动驾驶车辆等，其控制设计一直是一个活跃的领域。在趋同、同步、聚类和蜂拥等问题上已经有了很多结果。线性多智能体系统的协作式输出调节问题在近几年也有所研究，但非线性多智能体系统的协作式输出调节问题的结果却很少。在这篇论文中，我们专注于非线性多智能体系统的协作式输出调节问题，分别考虑了局部情况和全局情况。 / 非线性系统的输出调节问题旨在实现轨迹跟踪和不确定非线性被控对象产生的干扰抑制，其中参考输入和干扰是通过一定的动态系统产生的，被称为外部系统。众所周知，有两种解决经典输出调节问题的方法：前馈设计和内模设计。前馈设计使用调节方程的解设计控制律，而内模设计包括两个步骤。首先把被控对象的鲁棒输出调节问题转化成由被控对象和叫做内模的动态补偿器组成的增广系统的鲁棒镇定问题，然后鲁棒镇定增广系统。 / 不同于经典的输出调节问题，协作式输出调节问题处理由N个子系统组成的多智能体系统的渐近跟踪和干扰抑制问题。N个子系统的连接关系用信息图描述。我们可以把外部系统看作领导者，把N个子系统看作外部系统的追随者。根据是否是领导者的邻居，把N个追随者分为知情的追随者和不知情的追随者。知情的追随者一组是外部系统的邻居并且可以使用自己的信息设计控制器，而不知情的追随者一组不是外部系统的邻居并且可以用其邻居的信息进行控制设计。 / 基于经典输出调节问题的这两种方法，我们利用前馈方法考虑的非线性多智能体系统的局部协作式输出调节，和通过内模方法考虑全局情况。论文的主要贡献归纳如下。 / 1. 本文考虑了非线性多智能体系统的局部协作式输出调节问题，即为，设计一个分布式控制器使得整个闭环系统当外部信号设置为零时是渐近稳定的，并且初始条件足够小时输出误差渐进趋于零。由于不知情的追随者的控制器得不到外部信号，其对应的子系统的输出调节问题不能使用自己的状态设计控制器解决。这样输出调节问题就不能用前馈设计一个分散控制器解决。因此，我们考虑协作式控制以解决输出调节问题。为了克服上述困难，我们设计了分布式控制器，包括状态反馈控制器和可测输出反馈控制器。 / 2. 本文通过协作式内膜设计研究了非线性下三角多智能体系统的全局鲁棒输出调节问题。全局鲁棒输出调节问题定义如下：找到一个控制器使得被控对象在任何初始状态下闭环系统的轨迹存在并且有界，并且对所有的初始条件，跟踪误差渐近趋近于零。有两种方法可以解决网络系统的全局鲁棒输出调节问题：分散式方法和协作式方法。通过分散式方法，对每个子系统设计一个内模，这样其控制器的阶数和子系统的数量成正比。现在，通过共享不同的追随者之间的信息，我们将利用协作式方法对所有的子系统设计只有一个内模的控制器，从而得到一个所谓的协作式控制器。这个协作式控制器的阶数和追随者的数量是独立的，并且这种方法远远比分散式方法更直接。 / 最后，我们将使用一些例子来说明我们的两种设计方法的有效性。 / In recent years, the control design of multi-agent systems has been an active area due to its wide applications in the coordination and control of distributed sensor networks, mobile robots, autonomous vehicles, etc. Many results have been obtained in such issues as consensus, synchronization, swarming and flocking. The cooperative output regulation problem for linear multi-agent systems has been studied in recent years, but there are few results on cooperative output regulation of nonlinear multi-agent systems. In this thesis, we concentrate on the cooperative output regulation problem of nonlinear multi-agent systems and consider the local case and the global case, respectively. / The output regulation problem of nonlinear systems aims to achieve asymptotic tracking and disturbance rejection in an uncertain nonlinear plant where the reference inputs and disturbances are generated by an autonomous system called the exosystem. It is known that there are two methods for solving the classical output regulation problem: feed forward design and internal model design. The feed forward design makes use of the solution of the regulator equations to design a control law while the internal model design consists of two steps. The first one is to convert the robust output regulation problem for the given plant into a robust stabilization problem for an augmented system composed of the given plant and a dynamic compensator called internal model, and the second step aims to robustly stabilize the augmented system. / Different from the classical output regulation problem, the cooperative output regulation problem handles the asymptotic tracking and disturbance rejection problem of a system consisting of N subsystems, which is the multi-agent system we consider. The connection of N subsystems is described by an information graph. We can view the exosystem as a leader system and the N subsystems as followers of the exosystem. Depending on whether or not a follower is a neighbor of the leader, the N followers can be classified into the informed followers and the uninformed followers. The group of the informed followers is the set of the neighbors of the exosystem and can use its own information for the control design, while the uninformed followers are not the neighbors of the exosystem and can use their neighbors’ information for the control design. / Based on the two approaches for studying the classical output regulation problem, we consider the local cooperative output regulation for the nonlinear multi-agent system by a feed forward approach, and the global case by an internal model approach. The main contributions are summarized as follows. / 1. The local cooperative output regulation problem for nonlinear multi-agent systems is considered, that is, design a distributed control law such that the overall closed-loop system is asymptotically stable when the exosystem signal is set to zero and the error output approaches zero asymptotically for all suciently small initial conditions. Since the control law of the uninformed followers cannot access to the exogenous signal, the output regulation problem of each uninformed follower subsystem cannot be solved by a control law utilizing its own state. Thus the output regulation problem cannot be solved by a decentralized control scheme using the feedforward design. Therefore, we consider a cooperative control to solve the out¬put regulation problem. To overcome the above diculties, the distributed control schemes are designed, including the state feedback controller and the measurement output feedback controller. / 2. The global robust output regulation problem of nonlinear lower triangular multi-agent system with uncertainties via a cooperative internal model design is studied. The global robust output regulation problem is dened as follows: nd a control law such that the trajectory of the closed-loop system starting from any initial state of the plant exists and is bounded, and the tracking error approaches zero asymptotically for all initial conditions. There are two methods to solve global robust output regulation problem of the networked systems: decentralized method and cooperative method. From decentralized method, an internal model is designed for each subsystem, which leads to a control law whose order is proportional to the number of the subsystems. Here, by sharing the information among dierent followers, we will use cooperative method and manage to design a control law with one single internal model for all subsystems, thus leads to a so-called cooperative control law. The order of the cooperative control law is independent of the number of the followers, and is much more straightforward than the decentralized method. / Finally, we will use some examples to illustrate the eectiveness of our two design methods. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Han, Qiping. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 66-75). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.2 --- Contributions of Thesis --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Preliminaries --- p.6 / Chapter 2.1 --- Review of Graph Theory --- p.6 / Chapter 2.2 --- Fundamentals of Nonlinear Systems --- p.7 / Chapter 2.2.1 --- Lyapunov Stability --- p.7 / Chapter 2.2.2 --- Input-to-State Stability --- p.10 / Chapter 2.3 --- Feedforward Design of Nonlinear Output Regulation --- p.11 / Chapter 2.4 --- Internal Model Design of Nonlinear Output Regulation --- p.14 / Chapter 3 --- Local Cooperative Output Regulation --- p.22 / Chapter 3.1 --- Problem Formulation --- p.22 / Chapter 3.2 --- State Feedback Design --- p.25 / Chapter 3.3 --- Measurement Output Feedback Design --- p.34 / Chapter 3.4 --- Conclusions --- p.41 / Chapter 4 --- Global Robust Output Regulation via a Cooperative Controller --- p.42 / Chapter 4.1 --- Problem Formulation --- p.42 / Chapter 4.2 --- Solvability of the Problem --- p.49 / Chapter 4.3 --- Example --- p.54 / Chapter 4.4 --- Conclusions --- p.64 / Chapter 5 --- Conclusions --- p.65 / Bibliography --- p.66 / Biography --- p.76