Ph.D. (Computer Science) / The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of dominant paradigms which have incrementally improved the ease with which developers are able to express the logic and structure of their systems. The initially unstructured era of free-form spaghetti code gave way to structured programming in which the entry and exit points of functional units were well defined through the creation of abstractions such as procedures, sub-routines and functions. The problem of correctly associating data with the set of operations which are legal on this data was addressed through the concept of encapsulation with the onset of object-oriented programming. Object orientation also introduced a set of abstractions for safe code reuse through inheritance and dynamic polymorphism as well as composition/aggregation and delegation. The agent-oriented software development paradigm, when viewed as an extension of object orientation, adds the capacity of agent autonomy to an object, which allows it to select for itself which of its operations it will execute at any point in time. In addition, the separation between an agent and the environment within which it is embedded must be well defined. Agent autonomy allows for the modelling and development of loosely coupled systems with the capacity for complex emergent behaviour. The mapping of a given set of environmental percepts to an agent's operation selection defines its agent function and hence its emergent behaviour. Furthermore, agents may also be embedded into a shared environment together with other agents forming a multi-agent system. The emergent characteristics of such systems are defined not only through changes in environment state but also via agent to agent interactions. Multi-agent systems are categorised into cooperative or competitive based on whether all the agents within the system share a common goal. An argument is presented that even within cooperative multi-agent systems selfishness will emerge as a direct consequence of computational intractability. The core of the argument centres on the finite nature of the computational resources available to an agent which must be divided between the evaluation of the usefulness of other agent's knowledge and intentions towards improving the collective utility of the system and directly acting upon its own. As a direct result of the halting problem it is impossible for an agent to ascertain in general whether another agent's plans are even feasible (i.e. will result in the system reaching a goal state). As a direct consequence of such a limitation agents will in general favour their own courses of action over those of others and hence an emergent selfishness occurs even in ostensibly cooperative systems...

[EN] Over the last years, multiagent systems have been proven to be a powerful and versatile paradigm, with a big potential when it comes to solving complex problems in dynamic and distributed environments, due to their flexible and adaptive behavior. This potential does not only come from the individual features of agents (such as autonomy, reactivity or reasoning power), but also to their capability to communicate, cooperate and coordinate in order to fulfill their goals. In fact, it is this social behavior what makes multiagent systems so powerful, much more than the individual capabilities of agents. The social behavior of multiagent systems is usually developed by means of high level abstractions, protocols and languages, which normally rely on (or at least, benefit from) agents being able to communicate and interact indirectly. However, in the development process, such high level concepts habitually become weakly supported, with mechanisms such as traditional messaging, massive broadcasting, blackboard systems or ad hoc solutions. This lack of an appropriate way to support indirect communication in actual multiagent systems compromises their potential. This PhD thesis proposes the use of event tracing as a flexible, effective and efficient support for indirect interaction and communication in multiagent systems. The main contribution of this thesis is TRAMMAS, a generic, abstract model for event tracing support in multiagent systems. The model allows all entities in the system to share their information as trace events, so that any other entity which require this information is able to receive it. Along with the model, the thesis also presents an abstract architecture, which redefines the model in terms of a set of tracing facilities that can be then easily incorporated to an actual multiagent platform. This architecture follows a service-oriented approach, so that the tracing facilities are provided in the same way than other traditional services offered by the platform. In this way, event tracing can be considered as an additional information provider for entities in the multiagent system, and as such, it can be integrated from the earliest stages of the development process. / [ES] A lo largo de los últimos años, los sistemas multiagente han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos, gracias a su comportamiento flexible y adaptativo. Este potencial no es debido únicamente a las características individuales de los agentes (como son su autonomía, y su capacidades de reacción y de razonamiento), sino que también se debe a su capacidad de comunicación y cooperación a la hora de conseguir sus objetivos. De hecho, por encima de la capacidad individual de los agentes, es este comportamiento social el que dota de potencial a los sistemas multiagente. El comportamiento social de los sistemas multiagente suele desarrollarse empleando abstracciones, protocolos y lenguajes de alto nivel, los cuales, a su vez, se basan normalmente en la capacidad para comunicarse e interactuar de manera indirecta de los agentes (o como mínimo, se benefician en gran medida de dicha capacidad). Sin embargo, en el proceso de desarrollo software, estos conceptos de alto nivel son soportados habitualmente de manera débil, mediante mecanismos como la mensajería tradicional, la difusión masiva, o el uso de pizarras, o mediante soluciones totalmente ad hoc. Esta carencia de un soporte genérico y apropiado para la comunicación indirecta en los sistemas multiagente reales compromete su potencial. Esta tesis doctoral propone el uso del trazado de eventos como un soporte flexible, efectivo y eficiente para la comunicación indirecta en sistemas multiagente. La principal contribución de esta tesis es TRAMMAS, un modelo genérico y abstracto para dar soporte al trazado de eventos en sistemas multiagente. El modelo permite a cualquier entidad del sistema compartir su información en forma de eventos de traza, de tal manera que cualquier otra entidad que requiera esta información sea capaz de recibirla. Junto con el modelo, la tesis también presenta una arquitectura {abs}{trac}{ta}, que redefine el modelo como un conjunto de funcionalidades que pueden ser fácilmente incorporadas a una plataforma multiagente real. Esta arquitectura sigue un enfoque orientado a servicios, de modo que las funcionalidades de traza son ofrecidas por parte de la plataforma de manera similar a los servicios tradicionales. De esta forma, el trazado de eventos puede ser considerado como una fuente adicional de información para las entidades del sistema multiagente y, como tal, puede integrarse en el proceso de desarrollo software desde sus primeras etapas. / [CAT] Al llarg dels últims anys, els sistemes multiagent han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexes a entorns dinàmics i distribuïts, gràcies al seu comportament flexible i adaptatiu. Aquest potencial no és només degut a les característiques individuals dels agents (com són la seua autonomia, i les capacitats de reacció i raonament), sinó també a la seua capacitat de comunicació i cooperació a l'hora d'aconseguir els seus objectius. De fet, per damunt de la capacitat individual dels agents, es aquest comportament social el que dóna potencial als sistemes multiagent. El comportament social dels sistemes multiagent solen desenvolupar-se utilitzant abstraccions, protocols i llenguatges d'alt nivell, els quals, al seu torn, es basen normalment a la capacitat dels agents de comunicar-se i interactuar de manera indirecta (o com a mínim, es beneficien en gran mesura d'aquesta capacitat). Tanmateix, al procés de desenvolupament software, aquests conceptes d'alt nivell son suportats habitualment d'una manera dèbil, mitjançant mecanismes com la missatgeria tradicional, la difusió massiva o l'ús de pissarres, o mitjançant solucions totalment ad hoc. Aquesta carència d'un suport genèric i apropiat per a la comunicació indirecta als sistemes multiagent reals compromet el seu potencial. Aquesta tesi doctoral proposa l'ús del traçat d'esdeveniments com un suport flexible, efectiu i eficient per a la comunicació indirecta a sistemes multiagent. La principal contribució d'aquesta tesi és TRAMMAS, un model genèric i abstracte per a donar suport al traçat d'esdeveniments a sistemes multiagent. El model permet a qualsevol entitat del sistema compartir la seua informació amb la forma d'esdeveniments de traça, de tal forma que qualsevol altra entitat que necessite aquesta informació siga capaç de rebre-la. Junt amb el model, la tesi també presenta una arquitectura abstracta, que redefineix el model com un conjunt de funcionalitats que poden ser fàcilment incorporades a una plataforma multiagent real. Aquesta arquitectura segueix un enfoc orientat a serveis, de manera que les funcionalitats de traça són oferides per part de la plataforma de manera similar als serveis tradicionals. D'aquesta manera, el traçat d'esdeveniments pot ser considerat com una font addicional d'informació per a les entitats del sistema multiagent, i com a tal, pot integrar-se al procés de desenvolupament software des de les seues primeres etapes. / Búrdalo Rapa, LA. (2016). TRAMMAS: Enhancing Communication in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61765 / TESIS

Through cooperation, agents can transcend their individual capabilities and achieve goals that would be unattainable otherwise. Existing multiagent planning work considers each agent’s action capabilities, but does not account for distributed knowledge and the incompatible views agents may have of the planning domain. These divergent views can be a result of faulty sensors, local and incomplete knowledge, and outdated information, or simply because each agent has conducted different inferences and their beliefs are not aligned. This thesis is concerned with Multi-Perspective Cooperative Planning (MPCP), the problem of synthesising a plan for multiple agents which share a goal but hold different views about the state of the environment and the specification of the actions they can perform to affect it. Reaching agreement on a mutually acceptable plan is important, since cautious autonomous agents will not subscribe to plans that they individually believe to be inappropriate or even potentially hazardous. We specify the MPCP problem by adapting standard set-theoretic planning notation. Based on argumentation theory we define a new notion of plan acceptability, and introduce a novel formalism that combines defeasible logic programming and situation calculus that enables the succinct axiomatisation of contradictory planning theories and allows deductive argumentation-based inference. Our work bridges research in argumentation, reasoning about action and classical planning. We present practical methods for reasoning and planning with MPCP problems that exploit the inherent structure of planning domains and efficient planning heuristics. Finally, in order to allow distribution of tasks, we introduce a family of argumentation-based dialogue protocols that enable the agents to reach agreement on plans in a decentralised manner. Based on the concrete foundation of deductive argumentation we analytically investigate important properties of our methods illustrating the correctness of the proposed planning mechanisms. We also empirically evaluate the efficiency of our algorithms in benchmark planning domains. Our results illustrate that our methods can synthesise acceptable plans within reasonable time in large-scale domains, while maintaining a level of expressiveness comparable to that of modern automated planning.

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.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Coalition stability is an important concept in coalition formation. One common assumption in many stability criteria in non-transferable utility games is that the preference relations of each agent is publicly known so that a coalition is said to be stable if there is no objection by any sub-group of agents according to the publicly known preferences. / However, in many software agent applications, this assumption is not true. Instead, agents are modeled as individuals with private belief and decisions are made according to those beliefs instead of common knowledge. There are two types of uncertainty here. First, uncertainty in beliefs regarding the environment means that agents are also uncertain about their preferences. Second, an agent's actions can be influenced by his belief regarding other agents' preferences. Such uncertainties have impacts on the coalition's stability which is not reflected in the current stability criteria. / In this thesis, we extend the classic stability concept of the non-transferable utility core by proposing new belief based stability criteria under uncertainty, and illustrate how the new concept can be used to analyze the stability of a new type of belief-based coalition formation game. Mechanisms for reaching solutions of the new stable criteria is proposed and a real life application example is studied. / Chan, Chi Kong. / Adviser: Ho-Fung Leung. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3594. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 101-103). / 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.

在过去十年左右的时间里, 随着在无线传感网络, 群体机器人及无人飞行器编队等问题的广泛应用, 多智能体系统的协作控制问题已成为控制理论中的一个热点问题. 本论文将研究两类基本的协作控制问题: 多智能体趋同问题与多智能体协作式输出调节问题. / 作为多智能体系统协作控制的一个基本问题, 多智能体趋同问题是其它诸如蜂拥, 聚类, 编队等协作控制问题的基础. 目前趋同问题主要分为两类: 无领导者的趋同问题与有领导者的趋同问题. 无领导者的趋同问题的控制目标是设计分布式的控制器使得所有子系统的状态渐近地趋于一个共同但未知的轨迹, 而有领导者的趋同问题的控制目标是设计分布式的控制器使得所有子系统的状态渐近地趋于一个特殊的轨迹, 这个轨迹由一个特别的被称作领导者的子系统产生. 分布式的控制器常常由系统的拓扑连接图决定.连接图一般是时变的, 并包含固定图与切换图作为特例. 本论文的第一部分, 我们将研究连续时间一般线性多智能体系统与离散时间一般线性多智能体系统在联合连通假设下的切换拓扑网络的趋同问题. 该问题包含其它一些特殊的诸如一阶积分器系统, 筒谐振子系统的趋同问题作为特例. 这一部分的主要贡献概括为以下两点: / 1. 研究连续时间线性临界稳定多智能体系统在切换网络下的两类趋同问题. 为研究这两类问题, 我们将首先建立一类包含Kronecker 乘积的线性切换系统的稳定性结果. 该系统特别之处在于其系统矩阵在任何时刻都可以不是Hurwitz 稳定的. 我们将结合Lyapunov 稳定性理论与一类适用于分段连续线性系统的广义Barbalat 引理来研究该切换系统的稳定性. 作为该稳定性结果的直接应用, 我们分别给出求解两类趋同问题的静态状态反馈控制率. 与现有结果比较, 该结果仅假设动态图是联合连通的, 因而严格弱化了动态图的假设. / 2. 研究离散时间线性多智能体系统在切换网络下的两类趋同问题. 在系统矩阵是临界稳定的假设下, 我们证明如果动态图是联合连通的, 则都存在静态的分布式状态反馈控制器以达到两类趋同. 该趋同分析是基于一类自守线性离散时间切换系统的稳定性结果. 研究该切换系统的稳定性的主要困难在于系统矩阵在任何时刻都可以不是Schur 稳定的. 我们将结合共同Lyapunov 函数与一些新技巧来实现稳定性分析. 该结果将包含一些现有结果作为特例. / 论文的第二部分将研究多智能体系统的协作式输出调节问题. 该问题允许各子系统有不同的动态, 允许各子系统模型存在不确定性, 并且允许各子系统存在外部干扰, 因此该问题的描述较有领导者的趋同问题更为一般于实际. 该问题的控制目标是要利用分布式的控制策略来实现不确定多智能体系统的渐近跟踪和干扰抑制. 由于该问题描述的一般性, 其结论将包含其它一些诸如趋同, 同步, 编队等多智能体协同控制问题作为特例.就技术路线而言, 我们将建立分布式的观测器与分布式的内模来处理该问题. 这部分的主要贡献总结为以下三点: / 1. 研究线性多智能体系统分别在静态与切换拓扑网络下的协作式输出调节问题. 全系统包含两类子系统. 第一类子系统可以接收到外部系统的信号, 而第二类子系统不能接收到外部系统的信号. 因此, 传统的集中式的控制器与分散式的控制器都不适用于该系统. 我们将建立分布式的观测器实现外部系统的信息从第一类子系统向第二类子系统传递. 对静态拓扑网络情况, 我们分别给出分布式动态全状态反馈控制器与分布式动态测量输出反馈控制器求解该问题的充分必要条件. 对切换拓扑网络情况, 我们则给出分布式动态全状态反馈控制器与包含前馈项的分布式动态测量输出反馈控制器求解该问题的充分条件. 该结果可以作为多智能体有领导者的趋同问题的直接推广,并将应用于求解群体机器人有领导者的编队问题. / 2. 研究不确定线性多智能体系统在静态拓扑网络下的协作式鲁棒输出调节问题. 相对前一问题, 该问题允许多智能体系统的模型具有不确定参数. 因此前馈设计方案不适用于该问题. 通过建立分布式的内模, 我们将该问题转换成其增广系统的同时极点配置问题. 利用LQR 设计方法, 我们分别给出分布式动态状态反馈控制器与分布式输出反馈控制器求解该问题的充分必要条件. 由于极点配置具有鲁棒性, 这两类控制器均能容忍系统不确定参数的微小变化. 该结果也包含一些有领导者的趋同问题作为特例. / 3. 研究一类具有参数不确定性的混杂的多智能体系统在静态拓扑网络下的协作式鲁棒输出调节问题. 与前一问题比较, 这里我们允许系统参数在一个任意大的规定的紧集内变化. 为实现这一目标, 我们引入一类新的内模, 它能将协作式输出调节问题转化成其增广系统的鲁棒镇定问题. 我们将结合共同高增益状态反馈技巧与分布式高增益观测器技巧来设计分布式动态输出反馈控制器以求解该问题, 并同时给出其可解性的充分必要条件. 该结果可应用于求解一大类不确定多智能体系统的有领导者的鲁棒趋同问题. / Over the past decade, the extensive applications of wireless sensor networks, cooperative robotics, unmanned aerial vehicle formations and so on have made the cooperative control of the multi-agent system a trendy topic. This thesis will concentrate on two basic cooperative control problems: consensus and cooperative output regulation. / Consensus problem is one of the basic cooperative control problems of multi-agent systems. It is the foundation of many other cooperative control problems such as flocking, rendezvous, and formation control. There are two types of consensus problems: leaderless consensus problem and leader-following consensus problem. While the leaderless consensus problem aims to design a distributed controller for a multi-agent system so that the states of all agents asymptotically approach a common trajectory, leader-following consensus problem further requires that the distributed controller is such that the states of all agents converge to a specified trajectory which is usually produced by another agent called leader. The distributed controller is defined by a communication graph which is in general time-varying and contains both the fixed graph and switching graph as special cases. In the first part of this thesis, we will consider these two consensus problems for both continuous-time and discrete-time general linear multi-agent systems subject to the switching network topology under the jointly connected assumption. Our problem formulation includes the consensus of many typical physical multi-agent systems such as single-integrators and harmonic oscillators as special cases. The main results of this part are summarized as follows: / 1. Two consensus problems of continuous-time marginally stable linear multi-agent systems under switching network topology are studied. We first establish a stability result for a class of linear switched systems involving Kronecker product. The problem is intriguing in that the system matrix does not have to be Hurwitz at any time instant. We then establish the main stability result by a combination of the Lyapunov stability analysis and a generalized Barbalat’s Lemma applicable to piecewise continuous linear systems. As applications of this stability result, we present two distributed static state feedback controllers to solve the two consensus problems, respectively. In contrast with existing results, our result only assumes that the dynamic graph is jointly connected which is strictly weaker than any other assumptions. / 2. Two consensus problems of linear discrete-time multi-agent systems under switching network topology are studied. Under the assumption that the system matrix is marginally stable, we show that both leaderless consensus problem and leaderfollowing consensus problem can be achieved via the distributed static state feedback controllers provided that the dynamic graph is jointly connected. The consensus analysis is based on the stability analysis of a class of linear autonomous discretetime switched systems. The main difficulty to overcome is that the system matrix of such linear switched system may not be Schur at any time instant. We combine the common Lyapunov function approach with some novel technique to complete such stability analysis. Our result contains several existing results as special cases. / The second part of this thesis addresses the cooperative output regulation of linear multi-agent systems. The formulation of the cooperative output regulation problem is much more general than the leader-following consensus problem in that it deals with agents with different dynamics, allows model uncertainty, and accommodates external disturbance. The direct objective of this problem is to handle the asymptotic tracking and disturbance rejection problem in an uncertain multi-agent system via a distributed control approach. Due to the generality of this problem formulation, our result will also contain many control problems of multi-agent systems such as consensus, synchronization, and formation as special cases, thus leading to a unified solution to several different control problems of multi-agent systems. Technically, the distributed observer and the distributed internal model will be established for handling this problem. The main contributions of this part are summarized as follows: / 1. The cooperative output regulation of linear multi-agent systems under both static and switching communication network topologies is studied. The overall system consists of two groups of subsystems. While the first group of subsystems can access the exogenous signal, the second cannot. As a result, the problem cannot be solved by either the centralized approach or the decentralized approach. A distributed observer is devised so that it can relay the information of the exosystem from the first group to the second group. For the static network case, we present the sufficient and necessary solvability conditions via distributed dynamic state feedback control law and the distributed dynamic measurement output feedback control law. For the switching network case, we give the sufficient solvability condition via distributed dynamic state feedback control law and the distributed dynamic measurement output feedback with feedforward control law. This result can be viewed as a generalization of some leader-following consensus problems of multi-agent systems. It can also be applied to solve the leader-following formation problem of a group of mobil robots. / 2. The cooperative robust output regulation problem of linear uncertain multi-agent systems under static network topology is studied. In this problem, the structural plant uncertainty is further taken into consideration. Then the feedforward design is no longer applicable to this problem. By utilizing a distributed internal model, this problem is converted into a simultaneous eigenvalue placement problem of the so called augmented system. Using the LQR design method, we present the sufficient and necessary solvability conditions of this problem via both distributed dynamic state feedback control law and distributed dynamic output feedback control law. Due to the robustness of eigenvalue placement, such control laws can tolerate small plant uncertainty. This result also contains the leader-following consensus problem for several systems as special cases. / 3. The cooperative robust output regulation of a class of heterogeneous linear multiagent systems with parameter uncertainties under static network topology is studied. In contrast with the previous problem, here we allow the plant uncertain parameters to lie on an arbitrarily large prescribed compact subset. For this purpose, we introduce a new type of internal model that allows the cooperative robust output regulation problem of the given plant to be converted into a robust stabilization problem of an augmented multi-agent system. We then solve this problem via distributed dynamic output feedback control law by combining a simultaneous high gain state feedback control technique and a distributed high gain observer technique. The sufficient and necessary solvability conditions are also given. A special case of our result leads to the solution of the leader-following robust consensus problem for a large class of uncertain multi-agent systems. / 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. / Detailed summary in vernacular field only. / Su, Youfeng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 155-164). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.2 --- Thesis Contributions --- p.5 / Chapter 1.3 --- Thesis Organization --- p.7 / Chapter 2 --- Preliminaries --- p.10 / Chapter 2.1 --- Review of Graph Notation --- p.10 / Chapter 2.2 --- Review of Linear Output Regulation --- p.12 / Chapter 3 --- Continuous-Time Consensus under Switching Network Topology --- p.20 / Chapter 3.1 --- Introduction --- p.20 / Chapter 3.2 --- A Stability Result --- p.22 / Chapter 3.3 --- Problem Statement --- p.29 / Chapter 3.4 --- Solvability of Two Continuous-Time Consensus Problems --- p.32 / Chapter 3.4.1 --- Leaderless Consensus --- p.32 / Chapter 3.4.2 --- Leader-Following Consensus --- p.35 / Chapter 3.5 --- Examples --- p.38 / Chapter 3.6 --- Conclusion --- p.43 / Chapter 4 --- Discrete-Time Consensus under Switching Network Topology --- p.44 / Chapter 4.1 --- Introduction --- p.44 / Chapter 4.2 --- Problem Statement --- p.45 / Chapter 4.3 --- A Stability Result --- p.48 / Chapter 4.4 --- Solvability of Two Discrete-Time Consensus Problems --- p.53 / Chapter 4.4.1 --- Leaderless Consensus --- p.53 / Chapter 4.4.2 --- Leader-Following Consensus --- p.55 / Chapter 4.5 --- Examples --- p.57 / Chapter 4.6 --- Conclusion --- p.64 / Chapter 5 --- Linear Cooperative Output Regulation under Static Network --- p.67 / Chapter 5.1 --- Introduction --- p.67 / Chapter 5.2 --- Problem Statement --- p.70 / Chapter 5.3 --- Solvability of the Problem --- p.71 / Chapter 5.3.1 --- Distributed State Feedback --- p.71 / Chapter 5.3.2 --- Distributed Measurement Output Feedback --- p.76 / Chapter 5.4 --- An Example --- p.82 / Chapter 5.5 --- Conclusion --- p.86 / Chapter 6 --- Linear Cooperative Output Regulation under Switching Network --- p.87 / Chapter 6.1 --- Introduction --- p.87 / Chapter 6.2 --- Problem Statement --- p.88 / Chapter 6.3 --- Solvability of the Problem --- p.90 / Chapter 6.3.1 --- Some Lemmas --- p.90 / Chapter 6.3.2 --- Distributed State Feedback --- p.96 / Chapter 6.3.3 --- Distributed Measurement Output Feedback with Feedforward --- p.97 / Chapter 6.4 --- Application to Leader-Following Consensus --- p.100 / Chapter 6.5 --- Two Examples --- p.103 / Chapter 6.6 --- Conclusion --- p.113 / Chapter 7 --- Linear Cooperative Robust Output Regulation: A Structurally Stable Approach --- p.114 / Chapter 7.1 --- Introduction --- p.114 / Chapter 7.2 --- Problem Statement --- p.116 / Chapter 7.3 --- Solvability of the Problem --- p.117 / Chapter 7.4 --- An Example --- p.123 / Chapter 7.5 --- Conclusion --- p.125 / Chapter 8 --- Cooperative Robust Output Regulation of Heterogeneous Linear Uncertain Multi-Agent Systems --- p.128 / Chapter 8.1 --- Introduction --- p.128 / Chapter 8.2 --- Problem Statement --- p.130 / Chapter 8.3 --- From Output Regulation to Stabilization --- p.131 / Chapter 8.4 --- Stabilization of the Augmented System --- p.134 / Chapter 8.4.1 --- Two Lemmas --- p.135 / Chapter 8.4.2 --- Stabilization via State Feedback --- p.137 / Chapter 8.4.3 --- Stabilization via Output Feedback --- p.140 / Chapter 8.5 --- Solvability of Cooperative Output Regulation --- p.142 / Chapter 8.6 --- Examples --- p.144 / Chapter 8.6.1 --- Leader-Following Tracking of Mass-Damper-Spring Systems --- p.145 / Chapter 8.6.2 --- Formation of Multi Vehicles with Unknown Amplitude Disturbance --- p.148 / Chapter 8.7 --- Conclusion --- p.151 / Chapter 9 --- Conclusions --- p.152 / Bibliography --- p.155 / Biography --- p.165

Since the sequential inference framework determines the number of total samples in real-time based on the history data, it yields quicker decision compared to its fixed-sample-size counterpart, provided the appropriate early termination rule. This advantage is particularly appealing in the system where data is acquired in sequence, and both the decision accuracy and latency are of primary interests. Meanwhile, the Internet of Things (IoT) technology has created all types of connected devices, which can potentially enhance the inference performance by providing information diversity. For instance, smart home network deploys multiple sensors to perform the climate control, security surveillance, and personal assistance. Therefore, it has become highly desirable to pursue the solutions that can efficiently integrate the classic sequential inference methodologies into the networked multi-agent systems. In brief, this thesis investigates the sequential hypothesis testing problem in multi-agent networks, aiming to overcome the constraints of communication bandwidth, energy capacity, and network topology so that the networked system can perform sequential test cooperatively to its full potential. The multi-agent networks are generally categorized into two main types. The first one features a hierarchical structure, where the agents transmit messages based on their observations to a fusion center that performs the data fusion and sequential inference on behalf of the network. One such example is the network formed by wearable devices connected with a smartphone. The central challenges in the hierarchical network arise from the instantaneous transmission of the distributed data to the fusion center, which is constrained by the battery capacity and the communication bandwidth in practice. Therefore, the first part of this thesis is dedicated to address these two constraints for the hierarchical network. In specific, aiming to preserve the agent energy, Chapter 2 devises the optimal sequential test that selects the "most informative" agent online at each sampling step while leaving others in idle status. To overcome the communication bottleneck, Chapter 3 proposes a scheme that allows distributed agents to send only one-bit messages asynchronously to the fusion center without compromising the performance. In contrast, the second type of networks does not assume the presence of a fusion center, and each agent performs the sequential test based on its own samples together with the messages shared by its neighbours. The communication links can be represented by an undirected graph. A variety of applications conform to such a distributed structure, for instance, the social networks that connect individuals through online friendship and the vehicular network formed by connected cars. However, the distributed network is prone to sub-optimal performance since each agent can only access the information from its local neighborhood. Hence the second part of this thesis mainly focuses on optimizing the distributed performance through local message exchanges. In Chapter 4, we put forward a distributed sequential test based on consensus algorithm, where agents exchange and aggregate real-valued local statistics with neighbours at every sampling step. In order to further lower the communication overhead, Chapter 5 develops a distributed sequential test that only requires the exchange of quantized messages (i.e., integers) between agents. The cluster-based network, which is a hybrid of the hierarchical and distributed networks, is also investigated in Chapter 5.

She, Yingni. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 74-78). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Scope --- p.2 / Chapter 1.2 --- Motivation --- p.5 / Chapter 1.3 --- Structure of the Thesis --- p.6 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Intelligent Agents and Multiagent Systems --- p.8 / Chapter 2.1.1 --- Intelligent Agents --- p.8 / Chapter 2.1.2 --- Multiagent Systems --- p.10 / Chapter 2.2 --- Minority Game --- p.13 / Chapter 2.2.1 --- Minority Game --- p.13 / Chapter 2.2.2 --- Characteristics of Minority Game --- p.14 / Chapter 2.2.3 --- Strategies for Agents in Minority Game --- p.18 / Chapter 2.3 --- Resource Allocation --- p.22 / Chapter 2.3.1 --- Strategies for Agents in Multiagent Resource Allocation --- p.23 / Chapter 3 --- Individual Agent´ةs Wealth in Minority Game --- p.26 / Chapter 3.1 --- The Model --- p.26 / Chapter 3.2 --- Motivation --- p.27 / Chapter 3.3 --- Inefficiency Information --- p.28 / Chapter 3.4 --- An Intelligent Strategy --- p.31 / Chapter 3.5 --- Experiment Analysis --- p.32 / Chapter 3.6 --- Discussions and Analysis --- p.35 / Chapter 3.6.1 --- Equivalence to the Experience method --- p.36 / Chapter 3.6.2 --- Impact of M' and S' --- p.38 / Chapter 3.6.3 --- Impact of M and S --- p.41 / Chapter 3.6.4 --- Impact of Larger Number of Privileged Agents --- p.48 / Chapter 3.6.5 --- Comparisons with Related Work --- p.48 / Chapter 4 --- An Adaptive Strategy for Resource Allocation --- p.53 / Chapter 4.1 --- Problem Specification --- p.53 / Chapter 4.2 --- An Adaptive Strategy --- p.55 / Chapter 4.3 --- Remarks of the Adaptive Strategy --- p.57 / Chapter 4.4 --- Experiment Analysis --- p.58 / Chapter 4.4.1 --- Simulations --- p.58 / Chapter 4.4.2 --- Comparisons with Related Work --- p.62 / Chapter 5 --- Conclusions and Future Work --- p.69 / Chapter 5.1 --- Conclusions --- p.69 / Chapter 5.2 --- Future Work --- p.71 / A List of Publications --- p.73 / Bibliography --- p.74

Within the artificial intelligence subfield of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more effective preemption policies. Three central contributions arise. The first is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more effective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantifiable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in effective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we offer a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake.