This dissertation aims at solving the consensus control problem of multi-agent systems with Lipschitz nonlinearity. This depends on the design of the controller that enables each agent or subsystem in multi-agent systems with Lipschitz nonlinearity to reach consensus; using the understanding of the agents' connection network from the knowledge of graph theory as well as the control system design strategy. The objective is achieved by designing a type of distributed control, namely the consensus control, which manipulates the relative information of each agent in a multi-agent systems in order to arrive at a single solution. In addition, containment control is also developed to solve containment problem. It is an extension of consensus control via leader-follower configuration, aimed at having each agent contained by multiple leaders in a multi-agent systems with Lipschitz nonlinearity. Four types of controllers are proposed - state-feedback consensus controller, observer-based consensus controller, state-feedback containment controller and observer-based containment controller; each provides the stability conditions based on Lyapunov stability analysis in time domain which enabled each agent or subsystem to reach consensus. The observer-based controllers are designed based on the consensus observer that is related to Luenberger observer. Linear Matrix Inequality (LMI) and Algebraic Riccati Equation (ARE) are utilized to obtain the solutions for the stability conditions. The simulation results of the proposed controllers and observers have been carried out to prove their theoretical validity. Several practical examples of flexible robot arm simulations are included to further validate the theoretical aspects of the thesis.

Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Similar structures can be seen in game environments, where agents work together as teams to compete with other teams. The different agents within the same team must, while maintaining their own ‘personality’, work together and coordinate with each other to achieve a common team goal. This research develops strategy-based multi-agent multi-team systems, where strategy is framed as an instrument at the team level to coordinate the multiple agents of a team in a cohesive way. A formal specification of strategy and strategy-based multi-agent multi-team systems is provided. A framework is developed called SiMAMT (strategy- based multi-agent multi-team systems). The different components of the framework, including strategy simulation, strategy inference, strategy evaluation, and strategy selection are described. A graph-matching approximation algorithm is also developed to support effective and efficient strategy inference. Examples and experimental results are given throughout to illustrate the proposed framework, including each of its composite elements, and its overall efficacy. This research make several contributions to the field of multi-agent multi-team systems: a specification for strategy and strategy-based systems, and a framework for implementing them in real-world, interactive-time scenarios; a robust simulation space for such complex and intricate interaction; an approximation algorithm that allows for strategy inference within these systems in interactive-time; experimental results that verify the various sub-elements along with a full-scale integration experiment showing the efficacy of the proposed framework.

Cooperative control of multi-agent systems (MASs) has been intensively investigated in the past decade. The task is always complicated for an individual agent, but can be achieved by collectively operating a group of agents in a reliable, economic and efficient way. Although a lot of efforts are being spent on improving MAS performances, much progress has yet to be developed on different aspects. This thesis aims to solve problems in the consensus control of multiple quadrotors and/or mobile robots considering irregular sampling controls, heterogeneous agent dynamics and the presence of model uncertainties and disturbances. The thesis proceeds with Chapter 1 by providing the literature review of the state-of-the-art development in the consensus control of MASs. Chapter 2 introduces experimental setups of the laboratory involving two-wheeled mobile robots (2WMRs), quadrotors, positioning systems and inter-vehicle communications. All of the developed theoretical results in Chapters 3-6 are experimentally verified on the platform. Then it is followed by two main parts: Irregular sampling consensus control methods (Chapter 3 and 4) and cooperative control of heterogeneous MASs (Chapter 5 and 6). Chapter 3 focuses on the non-uniform sampling consensus control for a group of 2WMRs, and Chapter 4 studies the event-based rendezvous control for a group of asynchronous robots with time-varying communication delays. Chapter 5 concentrates on cooperative control methods for a heterogeneous MAS consisting of quadrotors and 2WMRs. Chapter 6 focuses on the design of a quadrotor flight controller which is robust to various adverse factors such as model uncertainties and external disturbances. The developed controller is further applied to the consensus control of the heterogeneous MAS. Specifically, Chapter 3 studies synchronized and non-periodical sampling consensus control methods for a group of 2WMRs. The directed and switching communication topologies among the network are considered in the controller design. The 2WMR is an underactuated system, which implies that it can not generate independent x and y accelerations in the two-dimensional plane. The rendezvous control methods are proposed for 2WMRs. The algebraic graph theory and stochastic matrix analysis are employed to conduct the convergence analysis. Although the samplings in the work of Chapter 3 are aperiodic, one feature is that local clocks of agents are required to be synchronized. Challenges arise in the practical control of distributed MASs, especially in the scenario that the global clock is lacking. Moreover, frequent samplings can result in redundant information transmissions when the communication bandwidth is limited. To address these problems, Chapter 4 investigates an event-based rendezvous control method for a group of asynchronous MAS with time-varying communication delays. Integral-type triggering conditions for each robot are adopted to be checked periodically. If the triggering condition is satisfied at one checking instant, the agent samples and broadcasts the state to the neighbors with a bounded communication delay. Then an algorithm is provided for driving 2WMRs to asymptotically reach rendezvous. The convergence analysis is conducted through Lyapunov approaches. Most of the theoretical works on cooperative control are focused on controlling agents with identical dynamics. However, in certain realistic scenarios, some complex missions require the cooperation of different types of agent dynamics such as surveillance, search and rescue, etc. Tasks can be carried out with higher efficiency by employing both the autonomous ground vehicles and unmanned aerial vehicles. To achieve better performance for MASs, in Chapter 5, distributed cooperative control methods for a heterogeneous MAS consisting of quadrotors and 2WMRs are developed. Consensus conditions are provided, and the theoretical results are experimentally verified. Many existing quadrotor control methods need exact model parameters of the quadrotor. In reality, when a quadrotor is conducting some tasks with extra payloads or with unexpected damages to the model structure, errors in parameters could result in the failure of the flight. External disturbances also inevitably affect the flight performance. To move a step further towards practical applications, in Chapter 6, a robust quadrotor flight controller using Integral Sliding Mode Control (ISMC) technique is investigated. In experiments, an extra payload with the position and mass unknown, is attached to destroy the accuracy of the model and to add disturbances. The designed controller significantly rejects negative effects caused by the payload during the flight. This controller is also successfully applied to an MAS consisting of a quadrotor and 2WMRs. / Graduate

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

Hoy en día los sistemas informáticos complejos se pueden ven en términos de los servicios que ofrecen y las entidades que interactúan para proporcionar o consumir dichos servicios. Los sistemas multi-agente abiertos, donde los agentes pueden entrar o salir del sistema, interactuar y formar grupos (coaliciones de agentes u organizaciones) de forma dinámica para resolver problemas, han sido propuestos como una tecnología adecuada para implementar este nuevo paradigma informático. Sin embargo, el amplio dinamismo de estos sistemas requiere que los agentes tengan una forma de armonizar los conflictos que surgen cuando tienen que colaborar y coordinar sus actividades. En estas situaciones, los agentes necesitan un mecanismo para argumentar de forma eficiente (persuadir a otros agentes para que acepten sus puntos de vista, negociar los términos de un contrato, etc.) y poder llegar a acuerdos. La argumentación es un medio natural y efectivo para abordar los conflictos y contradicciones del conocimiento. Participando en diálogos argumentativos, los agentes pueden llegar a acuerdos con otros agentes. En un sistema multi-agente abierto, los agentes pueden formar sociedades que los vinculan a través de relaciones de dependencia. Estas relaciones pueden surgir de sus interacciones o estar predefinidas por el sistema. Además, los agentes pueden tener un conjunto de valores individuales o sociales, heredados de los grupos a los que pertenecen, que quieren promocionar. Las dependencias entre los agentes y los grupos a los que pertenecen y los valores individuales y sociales definen el contexto social del agente. Este contexto tiene una influencia decisiva en la forma en que un agente puede argumentar y llegar a acuerdos con otros agentes. Por tanto, el contexto social de los agentes debería tener una influencia decisiva en la representación computacional de sus argumentos y en el proceso de gestión de argumentos. / Heras Barberá, SM. (2011). Case-Based Argumentation in Agent Societies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12497 / Palancia

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.

Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent. / Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el compor / Probablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més al / Alfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497 / TESIS

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