Cette thèse traite de l'automatisation de la généralisation cartographique qui est le procédé de simplification d'une base de données géographique vectorielle pour sa représentation sur une carte lisible. La recherche dans le domaine a abouti aujourd'hui au développement de nombreux processus automatiques de généralisation cartographique, chacun étant spécialisé pour un problème particulier comme un type de paysage, un thème de donnée, un type de conflit ou un mélange des trois (proximité entre bâtiments en zone urbaine). L'objectif de cette thèse est de tirer parti de cette diversité pour mettre en place la généralisation complète d'une carte en faisant collaborer des processus de généralisation complémentaires. Pour répondre à cet objectif, nous proposons le modèle CollaGen (Collaborative Generalisation) qui permet, par un système multi-agent, la collaboration des processus : les données sont découpées de manière pertinente par rapport aux processus à disposition en espaces géographiques (une zone urbaine ou le réseau routier par exemple) ; la généralisation d'un espace par un processus est ensuite orchestrée par CollaGen. CollaGen associe de manière itérative un espace à généraliser et un processus adapté, notamment par un mécanisme de registre type pages jaunes. L'interopérabilité entre les processus est assurée par une ontologie du domaine sur laquelle s'appuie un format de spécifications formelles d'une carte généralisée. Chaque généralisation est évaluée globalement en temps réel pour permettre un retour en arrière en cas de problème. Enfin, du fait du principe de découpage en espaces, CollaGen doit vérifier après chaque généralisation si des effets de bord sont apparus avec les objets géographiques situés juste à l'extérieur de l'espace, auquel cas il les corrige au mieux. Dans, cette thèse, le modèle CollaGen est mis en œuvre pour la généralisation de cartes topographiques (notamment au 1 : 50000) et les résultats sont comparés à d'autres approches et discutés / This phd thesis deals with cartographic generalisation, the process that simplifies a geographic database to allow its representation on legible map. Past research lead to the development of many automatic generalisation processes, each one being specialised for a specific problem like a particular landscape, a given data theme, a particular graphic conflict or a mix of the three (like ‘proximity between buildings in urban areas). The aim of the thesis is to benefit from this diversity to carry out a complete map generalisation by collaboration between complementary processes. To meet this objective, the CollaGen model is proposed (Collaborative Generalisation) as it allows, based on multi-agent techniques, generalisation processes collaboration : data is relevantly partitioned into geographic spaces (e.g. an urban area or the road network) ; then CollaGen orchestrate the generalisation of a space by an adapted process. CollaGen iterately maps a space to be generalised and an adapted process thanks to a yellow pages registry mecanism. The interoperability between processes is managed by a domain ontology on which formal map specifications are based. Each generalization is globally assessed online to allow backtracks if necessary. Finally, because of the space partitioning, CollaGen has to check after each generalisation if side effects appeared with spaces just outside the one that has been generalised. If some side effects occurred, they are corrected. In this thesis, CollaGen is implemented for topographic map generalisation (to 1 : 50000) and results obtained are compared to other approaches and discussed

Orientador: Edmundo Roberto Mauro Madeira / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T03:57:24Z (GMT). No. of bitstreams: 1 SoaresJunior_MiltonAparecido_M.pdf: 2099044 bytes, checksum: 9429a525b941834e987d70cb3b26c2fc (MD5) Previous issue date: 2013 / Resumo: Apesar do seu sucesso, a arquitetura atual da Internet _e uma fonte de vários problemas para as aplicações atuais e as demandas futuras. A virtualização da infraestrutura da rede é proposta como alternativa para solucionar esses problemas sem a necessidade de alterar o núcleo da Internet, pois ela habilita o pluralismo de arquiteturas de rede. Neste trabalho, foi desenvolvida uma arquitetura de gerência autonômica de redes virtuais baseada em sistemas multiagentes. Um protótipo que realiza a função de autocura de redes virtuais foi implementado a partir dessa arquitetura. Novos algoritmos e mecanismos foram desenvolvidos para melhorar a eficiência do protótipo. Foi realizado, também, um estudo de caso sobre a gerência de redes virtuais que leva em consideração os requisitos das aplicações que estão sendo executadas em uma nuvem. Uma plataforma de experimentação baseada em máquinas virtuais e no OpenFlow foi criada para a execução dos experimentos. Tanto o protótipo quanto a plataforma de experimentação integram ferramentas atuais criando uma única solução para a gerência de redes virtuais. Os resultados apresentados contribuem para aproximar a virtualização de redes e a gerência autonômica da realidade / Abstract: Despite its success, the current architecture of the Internet is a source of many problems for current applications and future demands. The virtualization of network infrastructure is proposed as an alternative to solve these problems without the need to change the core of the Internet, as it enables the network architecture pluralism. We have developed architecture for autonomic management of virtual networks based on multi-agent systems. Based on this architecture, we implemented a prototype that performs the function of self-healing virtual networks. New algorithms and mechanisms have been developed to improve the efficiency of the prototype. A case study on the management of virtual networks that takes into consideration the requirements of the applications that are running on a cloud is also presented. For the execution of the experiments was created an experimentation platform based on virtual machines and on OpenFlow. The prototype and the platform integrate current tools creating a single solution for management of virtual networks. The results contributed to bring network virtualization and autonomic management closer to reality / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

abstract: The problem of modeling and controlling the distribution of a multi-agent system has recently evolved into an interdisciplinary effort. When the agent population is very large, i.e., at least on the order of hundreds of agents, it is important that techniques for analyzing and controlling the system scale well with the number of agents. One scalable approach to characterizing the behavior of a multi-agent system is possible when the agents' states evolve over time according to a Markov process. In this case, the density of agents over space and time is governed by a set of difference or differential equations known as a {\it mean-field model}, whose parameters determine the stochastic control policies of the individual agents. These models often have the advantage of being easier to analyze than the individual agent dynamics. Mean-field models have been used to describe the behavior of chemical reaction networks, biological collectives such as social insect colonies, and more recently, swarms of robots that, like natural swarms, consist of hundreds or thousands of agents that are individually limited in capability but can coordinate to achieve a particular collective goal. This dissertation presents a control-theoretic analysis of mean-field models for which the agent dynamics are governed by either a continuous-time Markov chain on an arbitrary state space, or a discrete-time Markov chain on a continuous state space. Three main problems are investigated. First, the problem of stabilization is addressed, that is, the design of transition probabilities/rates of the Markov process (the agent control parameters) that make a target distribution, satisfying certain conditions, invariant. Such a control approach could be used to achieve desired multi-agent distributions for spatial coverage and task allocation. However, the convergence of the multi-agent distribution to the designed equilibrium does not imply the convergence of the individual agents to fixed states. To prevent the agents from continuing to transition between states once the target distribution is reached, and thus potentially waste energy, the second problem addressed within this dissertation is the construction of feedback control laws that prevent agents from transitioning once the equilibrium distribution is reached. The third problem addressed is the computation of optimized transition probabilities/rates that maximize the speed at which the system converges to the target distribution. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2020

[pt] Sistemas multiagentes abertos são sociedades em que os agentes autônomos e heterogêneos podem trabalhar para fins semelhantes ou diferentes. A fim de lidar com a heterogeneidade, autonomia e diversidade de interesses entre os diferentes membros, os sistemas estabelecem um conjunto de normas que é usado como um mecanismo de controle social para garantir uma ordem social desejável, em que os agentes trabalhem em conjunto. Tais normas regulam o comportamento dos agentes, definindo obrigações, permissões e proibições. Além disso, as normas podem dar estímulo para a sua realização através da definição de recompensas e pode desencorajar a sua violação, através de punições. Embora as normas sejam promissores mecanismos que regulam o comportamento dos agentes, deve-se levar em conta que os agentes são entidades autônomas, de modo que devem ser livres para decidir cumprir ou violar cada norma. Portanto, os agentes podem utilizar diferentes estratégias para alcançar seus objetivos e cumprir com as normas dirigidas a eles. De um lado, os agentes podem escolher atingir seus objetivos sem se preocupar com suas normas, ou seja, sem se preocupar com as recompensas que poderiam receber se cumprissem as normas ou as punições que receberão por violá-las. Por outro lado, alguns agentes escolherão cumprir com todas as normas embora alguns dos seus objetivos não possam ser alcançados. Neste contexto, este trabalho propõe um framework para simulação de agentes normativos que provê os mecanismos necessários para compreender os impactos das normas sobre os agentes que adotam algumas dessas estratégias para lidar com as normas. A aplicabilidade do framework será avaliada em dois cenários de uso: o primeiro no contexto de prevenções de crimes e o segundo está relacionado a missões de resgate de civis que estão em áreas de risco. / [en] Open multi-agent systems are societies in which autonomous and heterogeneous agents can work towards similar or different ends. In order to cope with the heterogeneity, autonomy and diversity of interests among the different members, those systems establish a set of norms that is used as a mechanism of social control to ensure a desirable social order in which agents work together. Such norms regulate the behaviour of the agents by defining obligations, permissions and prohibitions. Moreover, norms may give stimulus to their fulfillment by defining rewards and may discourage their violation by stating punishments. Although norms are promising mechanisms to regulate agents’ behavior, we should take into account that agents are autonomous entity, so they must be free to decide to fulfill or violate each norm. In this way, agents can use different strategies when deciding to achieve their goals and comply with the norms addressed to themselves. On one hand, agents might choose to achieve their goals without concerning with their norms, i.e., without concerting with the rewards they could receive if they fulfill the norms and the punishments they will receive for violating them. On the other hand, some agents will choose to comply with all the norms although some of their goals may not be achieved. In this context, this work proposes a framework for simulating normative agents that provides the necessary mechanisms to understand the impacts of norms on agents that adopt some of those strategies to deal with norms. The applicability of the framework will be evaluated in two scenarios: the first in the context of prevention of crimes and the second is related to the mission of rescuing civilians who are at risk areas.

Cette thèse traite du problème où une équipe d'agents coopératifs et autonomes, évoluant dans un environnement stochastique partiellement observable, et œuvrant à la résolution d'une tâche complexe, doit modifier dynamiquement sa composition durant l'exécution de la tâche afin de s'adapter à l'évolution de celle-ci. Il s'agit d'un problème qui n'a été que peu étudié dans le domaine de la planification multi-agents. Pourtant, il existe de nombreuses situations où l'équipe d'agent mobilisée est amenée à changer au fil de l'exécution de la tâche.Nous nous intéressons plus particulièrement au cas où les agents peuvent décider d'eux-même de quitter ou de rejoindre l'équipe opérationnelle. Certaines fois, utiliser peu d'agents peut être bénéfique si les coûts induits par l'utilisation des agents sont trop prohibitifs. Inversement, il peut parfois être utile de faire appel à plus d'agents si la situation empire et que les compétences de certains agents se révèlent être de précieux atouts.Afin de proposer un modèle de décision qui permette de représenter ces situations, nous nous basons sur les processus décisionnels de Markov décentralisés et partiellement observables, un modèle standard utilisé dans le cadre de la planification multi-agents sous incertitude. Nous étendons ce modèle afin de permettre aux agents d'entrer et sortir du système. On parle alors de système ouvert. Nous présentons également deux algorithmes de résolution basés sur les populaires méthodes de recherche arborescente Monte-Carlo. Le premier de ces algorithmes nous permet de construire des politiques jointes séparables via des calculs de meilleures réponses successives, tandis que le second construit des politiques jointes non séparables en évaluant les équipes dans chaque situation via un système de classement Elo. Nous évaluons nos méthodes sur de nouveaux jeux de tests qui permettent de mettre en lumière les caractéristiques des systèmes ouverts. / This thesis addresses the problem where a team of cooperative and autonomous agents, working in a stochastic and partially observable environment towards solving a complex task, needs toe dynamically modify its structure during the process execution, so as to adapt to the evolution of the task. It is a problem that has been seldom studied in the field of multi-agent planning. However, there are many situations where the team of agents is likely to evolve over time.We are particularly interested in the case where the agents can decide for themselves to leave or join the operational team. Sometimes, using few agents can be for the greater good. Conversely, it can sometimes be useful to call on more agents if the situation gets worse and the skills of some agents turn out to be valuable assets.In order to propose a decision model that can represent those situations, we base upon the decentralized and partially observable Markov decision processes, the standard model for planning under uncertainty in decentralized multi-agent settings. We extend this model to allow agents to enter and exit the system. This is what is called agent openness. We then present two planning algorithms based on the popular Monte-Carlo Tree Search methods. The first algorithm builds separable joint policies by computing series of best responses individual policies, while the second algorithm builds non-separable joint policies by ranking the teams in each situation via an Elo rating system. We evaluate our methods on new benchmarks that allow to highlight some interesting features of open systems.

The sixth-generation (6G) of wireless communication systems will significantly rely on fog/edge network architectures for service provisioning. To satisfy stringent quality of service requirements using dynamically available resources at the edge, new network access schemes are needed. In this paper, we consider a cognitive dynamic edge/fog network where primary users (PUs) may temporarily share their resources and act as fog nodes for secondary users (SUs). We develop strategies for distributed dynamic fog probing so SUs can find out available connections to access the fog nodes. To handle the large-state space of the connectivity availability that includes availability of channels, computing resources, and fog nodes, and the partial observability of the states, we design a novel distributed Deep Q-learning Fog Probing (DQFP) algorithm. Our goal is to develop multi-user strategies for accessing fog nodes in a distributed manner without any centralized scheduling or message passing. By using cooperative and competitive utility functions, we analyze the impact of the multi-user dynamics on the connectivity availability and establish design principles for our DQFP algorithm.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23320号 / 情博第756号 / 新制||情||129(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 伊藤 孝行, 教授 畑山 満則 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Industrial operational environments are stochastic and can have complex system dynamics which introduce multiple levels of uncertainty. This uncertainty leads to sub-optimal decision making and resource allocation. Digitalisation and automation of production equipment and the maintenance environment enable predictive maintenance, meaning that equipment can be stopped for maintenance at the optimal time. Resource constraints in maintenance capacity could however result in further undesired downtime if maintenance cannot be performed when scheduled. In this dissertation the applicability of using a Multi-Agent Deep Reinforcement Learning based approach for decision making is investigated to determine the optimal maintenance scheduling policy in a fleet of assets where there are maintenance resource constraints. By considering the underlying system dynamics of maintenance capacity, as well as the health state of individual assets, a near-optimal decision making policy is found that increases equipment availability while also maximising maintenance capacity. The implemented solution is compared to a run-to-failure corrective maintenance strategy, a constant interval preventive maintenance strategy and a condition based predictive maintenance strategy. The proposed approach outperformed traditional maintenance strategies across several asset and operational maintenance performance metrics. It is concluded that Deep Reinforcement Learning based decision making for asset health management and resource allocation is more effective than human based decision making. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2020. / Mechanical and Aeronautical Engineering / MEng (Mechanical Engineering) / Unrestricted

Decentralized multi-agent optimization has emerged as a powerful paradigm that finds broad applications in engineering design including federated machine learning and control of networked systems. In these setups, a group of agents are connected via a network with general topology. Under the communication constraint, they aim to solving a global optimization problem that is characterized collectively by their individual interests. Of particular importance are the computation and communication efficiency of decentralized optimization algorithms. Due to the heterogeneity of local objective functions, fostering cooperation across the agents over a possibly time-varying network is challenging yet necessary to achieve fast convergence to the global optimum. Furthermore, real-world communication networks are subject to congestion and bandwidth limit. To relieve the difficulty, it is highly desirable to design communication-efficient algorithms that proactively reduce the utilization of network resources. This dissertation tackles four concrete settings in decentralized optimization, and develops four provably efficient algorithms for solving them, respectively. Chapter 1 presents an overview of decentralized optimization, where some preliminaries, problem settings, and the state-of-the-art algorithms are introduced. Chapter 2 introduces the notation and reviews some key concepts that are useful throughout this dissertation. In Chapter 3, we investigate the non-smooth cost-coupled decentralized optimization and a special instance, that is, the dual form of constraint-coupled decentralized optimization. We develop a decentralized subgradient method with double averaging that guarantees the last iterate convergence, which is crucial to solving decentralized dual Lagrangian problems with convergence rate guarantee. Chapter 4 studies the composite cost-coupled decentralized optimization in stochastic networks, for which existing algorithms do not guarantee linear convergence. We propose a new decentralized dual averaging (DDA) algorithm to solve this problem. Under a rather mild condition on stochastic networks, we show that the proposed DDA attains an $\mathcal{O}(1/t)$ rate of convergence in the general case and a global linear rate of convergence if each local objective function is strongly convex. Chapter 5 tackles the smooth cost-coupled decentralized constrained optimization problem. We leverage the extrapolation technique and the average consensus protocol to develop an accelerated DDA algorithm. The rate of convergence is proved to be $\mathcal{O}\left( \frac{1}{t^2}+ \frac{1}{t(1-\beta)^2} \right)$, where $\beta$ denotes the second largest singular value of the mixing matrix. To proactively reduce the utilization of network resources, a communication-efficient decentralized primal-dual algorithm is developed based on the event-triggered broadcasting strategy in Chapter 6. In this algorithm, each agent locally determines whether to generate network transmissions by comparing a pre-defined threshold with the deviation between the iterates at present and lastly broadcast. Provided that the threshold sequence is summable over time, we prove an $\mathcal{O}(1/t)$ rate of convergence for convex composite objectives. For strongly convex and smooth problems, linear convergence is guaranteed if the threshold sequence is diminishing geometrically. Finally, Chapter 7 provides some concluding remarks and research directions for future study. / Graduate

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2019