Uma cadeia de suprimentos é uma rede composta por fornecedores, plantas de manufatura, depósitos, centros de distribuição e varejistas através da qual matériasprimas são adquiridas, transformadas e entregue aos consumidores. A gestão de cadeias de suprimentos (do inglês, Supply Chain Management - SCM) envolve a tomada de decisão nos níveis estratégico, tático e operacional, cujo objetivo é otimizar o desempenho da cadeia . O desenvolvimento de modelos para a avaliação de cadeias de suprimento é uma boa alternativa para estudar a gestão da demanda por produtos, bem como para analisar a efetividade de políticas de gerenciamento. Sistemas multiagentes são apropriados para estudar cadeias de suprimento, pois as diferentes unidades de negócio envolvidas podem ser modeladas como agentes autônomos, assim como suas regras de gerenciamento. Além disso, a modelagem do fluxo de produtos e do fluxo de informações, tais como volume de pedidos e prazos de entrega de um elo da cadeia para outro é facilitada. A análise da literatura especializada da área demonstra que a maior parte dos estudos procura resolver problemas específicos e sem considerar agentes normativos interferindo no comportamento individual de cada ator da cadeia. Este trabalho procura contribuir com o estado da arte da área de gestão de cadeias de suprimento da seguinte forma: (1) propondo um modelo de simulação, composto por agentes genéricos que podem ser facilmente estendidos e utilizados e outros contextos de aplicação, e (2) propondo a utilização dos conceitos de agentes normativos no contexto de cadeias de suprimento. A união dessas áreas (SCM e sistemas multiagentes normativos) aumenta as possibilidades de modelagem de cadeias de suprimento, permitindo a inclusão de entidades externas que normalmente exercem influência na gestão, tais como órgãos do governo, agências reguladoras e instituições eletrônicas. A modelagem da cadeia do biodiesel é apresentada como estudo de caso; os principais resultados obtidos são apresentados e discutidos. / The supply chain is a network of suppliers, factories, warehouses, distribution centers, and retailers through which raw materials are acquired, transformed, and delivered to customers. Supply-chain management (SCM) is the strategic, tactical, and operational decision making that optimizes supply-chain performance. Modeling supply chain is a good way of studying order fulfillments processes and investigating the effectiveness of management policies. Multiagentes models are increasingly being used of this purpose. A multiagent model fits well with the task of simulation supply chain because the businesses involved can be modeled as agents, each with its own inventory rules. It is also easy to model the flow of products down the chain and the flow of information, such as order volumes and lead times, from one organization to another. The analysis of the relevant literature shows that most research works carried out in this area aim to resolve specific problems. Some methodologies and more generic solutions have been proposed, but without considering normative agents which may interfere in the behaviour of actors of the supply chain. This work aims to contribute with the state of the art in the SCM area as follows: (1) building a simulation model to the supply chain context, providing generic agents which may be easily extended and used in other application contexts, and (2) exploiting normative agents in the context of supply chain modeling. The integration of these areas (SCM and normative multi-agent systems) increases the possibilities of supply chain modeling, allowing the inclusion of external entities which normally influence management, such as governmental organizations, regulating agencies and electronic institutions, to give some examples. The modeling of the biodiesel supply chain is presented as a case study; the main results are presented and discussed.

This work presents the concept of code and data propagation in a multi-agent system. First, the concepts of agent and multi-agent system are defined and examples are presented. Also arguments for using agent approach are given and potential benefits are listed. Afterwards the idea of code and data propagation is defined and explained. Then some examples of real solutions are given along with propagation algorithms, which depict the way of introducing the concept into real system solutions. Afterwards the code and data propagation in a multi-agent system is described, which is in many cases based on the object migration. Discussion about this concept ends with describing the types of agent migration and giving some examples of systems with agent migration. Then three multi-agent environments are described and one is chosen to be the basis of the implemented application. Second part of work starts with description of systems principal objective, which is the distributed calculating of prime numbers. At the beginning, agents of the system are presented, and then the main system processes are depicted in detail. All algorithms are shown in sequence diagrams, which point all asynchronisms in the system. Afterwards migration phase is described with all migration types and algorithms. The experiments are conducted in two environments: home and university. The main aim is to find optimal configurations for both environments. The conclusion from this work is that introducing code and data propagation to a multiagent system in a form of agent migration in a heterogeneous network could considerably decrease the execution time. Moreover based on the efficiency vector of computers participating in the experiment there is a possibility to set a task distribution, which is close to optimal, without searching for optimal configuration every time when running the experiment.

Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk. / I brädspelet Risk är det svårt att använda traditionella Artificiell-Intelligens-metoder eftersom sökrymden är extremt stor. Lösningen till detta kan vara att använda distribuerad problemlösning i form av ett multi-agent system. Detta måste testas innan man kan säga om ett multi-agent system är framgångsrikt, eller ej, i att spela Risk. Denna uppsats går igenom utvecklingen av ett multi-agent system som spelar Risk. Systemet placerar en agent i varje land på brädet och använder en central agent för att organisera kommunikationen. En auktionsmekanism används vid förhandlingar. Experimenten visar att ett multi-agent system är en framgångsrik infallsvinkel vid utveckling av en datorbaserad spelare för brädspelet Risk.

Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk. / I brädspelet Risk är det svårt att använda traditionella Artificiell-Intelligens-metoder eftersom sökrymden är extremt stor. Lösningen till detta kan vara att använda distribuerad problemlösning i form av ett multi-agent system. Detta måste testas innan man kan säga om ett multi-agent system är framgångsrikt, eller ej, i att spela Risk. Denna uppsats går igenom utvecklingen av ett multi-agent system som spelar Risk. Systemet placerar en agent i varje land på brädet och använder en central agent för att organisera kommunikationen. En auktionsmekanism används vid förhandlingar. Experimenten visar att ett multi-agent system är en framgångsrik infallsvinkel vid utveckling av en datorbaserad spelare för brädspelet Risk.

An implementation of the Electric Field Approach applied to the simulated RoboCup is presented, together with a demonstration of a learning system. Results are presented from the optimization of the Electric Field parameters in a limited situation, using the learning system. Learning techniques used in contemporary RoboCup research are also described including a brief presentation of their results.

Les cartes représentent l'information géographique d'une zone donnée de manière d'autant plus simplifiée que l'échelle de la carte est petite. Le procédé de simplification, appelé généralisation cartographique, est soumis au respect de contraintes de lisibilité, d'adéquation de la représentation avec le niveau d'abstraction souhaité et de cohérence avec la réalité. La volonté d'automatiser le processus de création de cartes à partir de bases de données géographiques, a conduit à la création d'algorithmes permettant d'effectuer cette simplification objet par objet. Néanmoins, les choix des algorithmes, tout comme leur paramétrage, sont autant influencés par l'objet sur lequel ils s'appliquent que par les autres objets en relation (e.g. bâtiment à proximité d'un autre, route parallèle à un alignement de bâtiments). Ce constat a motivé l'utilisation de modèles multi-agents pour la généralisation automatisée de cartes. Le principe de ces modèles multi-agents repose sur la modélisation des objets (e.g. bâtiment, tronçon de route, îlot urbain) sous forme d'agents qui cherchent à se généraliser de façon à satisfaire leurs contraintes. Plusieurs modèles multi-agents ont été proposés, chacun ayant une approche différente des interactions entre niveaux. Ici, nous entendons par niveau, par exemple, la distinction entre les agents individuels comme un bâtiment, des agents représentant un groupe d’autres agents, comme un îlot urbain composé des routes l’entourant et des bâtiments inclus dans l’îlot.Nous étudions l'unification de ces modèles en nous appuyant sur le paradigme multi-niveaux PADAWAN, afin de faciliter les interactions entre agents de niveaux différents. Nous proposons ainsi le modèle DIOGEN, adaptant les principes d’interaction entre agents de niveaux différents à la généralisation cartographique guidée par des contraintes, ce qui a permis d’unifier les précédents modèles AGENT, CartACom et GAEL, tout en disposant de nouvelles capacités prometteuses.Nous avons évalué notre proposition sur un ensemble de cas d’étude. Parmi ces cas, nous nous sommes penchés sur la généralisation de carte de randonnée, où les itinéraires sont symbolisés individuellement avec des symboles différents, à la manière des plans de bus. La présence de plusieurs symboles d’itinéraires sur une même route support amène des problèmes de généralisation particuliers, comme le choix du positionnement des itinéraires de part et d’autre de la route, ou les implications pour les autres objets de la carte (e.g. points d’intérêts, bâtiments) se retrouvant sous le symbole de l’itinéraire, problèmes que nous essayons de résoudre en nous appuyant sur notre proposition de représentation formelle multi-niveaux.Ce travail nous a ensuite conduit à identifier des comportements multi-niveaux récurrents. Nous les avons exprimés de façon générique sous forme de patterns d’analyse, affranchies des spécificités de la généralisation cartographique, et de la résolution de problèmes contraints / Maps show geographic information of a given area in a simplified way, particularly when the scale is small. The simplification process, called cartographic generalisation, is submitted to several constraints : legibility, adequation to the abstraction level, and consistency with reality. The will to automate the maps creation process from geographical databases led to the creation of algorithms allowing the simplification object by object. However the choice of the algorithms, as their settings, are influenced by the object on which it is applied, and by the other objects in relation with this object (e.g. a building close to another one, a road parallel to a buildings alignment). This motivates the use of multi-agents models for automated map generalisation. Several multi-agent models were proposed, each of them having a different approach to manage multi-levels relations. Here, what we call a level is, for instance, the distinction between individual agents, like a building, and agents representing a group of other agents, like a urban block composed by the surrounding roads and buildings inside.We study the unification of existing models, using the multi-level paradigm PADAWAN, in order to simplify interactions between agents in different levels. We propose the DIOGEN model, in which the principle of interactions between agents of different levels is adapted to cartographic generalisation guided by constraints, those allowing to unify the existing models AGENT, CartACom and GAEL, and giving promising features.We evaluate our proposal on different case studies. Among them, we study the generalisation of trekking maps, where the routes are symbolized individually by a different couloured line symbols, like on bus maps. The presence of several route symbols on a same road leads to specific generalisation issues, like the choice of the side of each route symbol position, or the implications for the other objects on the map (e.g. points of interest, buildings) under the route symbol – issues tackled using our proposal of formal multi-levels representation.This work leads us to the identification of recurrent behaviours. We express them as analysis patterns, in a way that is independent from cartographic generalisation and constraint solving problems

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Unexpected changes occurring in complex and dynamic domains render supporting systems unsuited to the new conditions. Examples of such domains include business ecosystems, digital service ecosystems, manufacturing, transport, and city modelling. These are regarded as ecosystem domains. Multi-agent systems are seen as an appropriate technology for their support, yet they lack the required ability to adapt to unexpected changes. The research presented in this thesis aims to create a multi-agent system based in-silico model endowed with the capability of adaptation to unexpected changes occurring in ecosystem domains. The approach taken consists of applying adaptation properties of complex adaptive systems, such as natural ecosystems, to multi-agent systems to create one which can cope with unexpected changes. A dynamic agent-based ecosystem model called DAEM is formalised by combining characteristics of natural ecosystem and principles of adaptive multi-agent systems. A set of experiments is presented using a DAEM prototype to demonstrate its resilience to unexpected changes in a hypothetical ecosystem. A comparison is made against a simulated typical solution for showing how DAEM is more resilient to unexpected changes than the typical approach. This supports the claim of this thesis that DAEM represents a significant contribution to knowledge. A software embodiment of DAEM to drive adaptation in ecosystem domains is presented and placed in an execution context evaluated by two practical examples of ecosystem domains. These show how DAEM suggests interactions to the supporting system of the execution context, and incorporates taken decisions into the ecosystem regarding interactions with other individuals. This supports the claim that the DAEM software embodiment is suitable for providing adaptation support in ecosystem domains, thus representing another significant contribution of this thesis. The contributions to knowledge of this thesis are then a) a formal model of a dynamic agent-based ecosystem called DAEM; and b) a software embodiment of DAEM, called DAEM layer, to support adaptation in ecosystem domains. Future work includes further tests to analyse patterns and make estimations in existing ecosystems, among others.

A lo largo de los últimos años, los sistemas multi-agente (SMA) han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos. Este potencial no se debe principalmente a sus características individuales (como son su autonomía, su capacidad de percepción, reacción y de razonamiento), sino que también a la capacidad de comunicación y cooperación a la hora de conseguir un objetivo. De hecho, su capacidad social es la que más llama la atención, es este comportamiento social el que dota de potencial a los sistemas multi-agente. Estas características han hecho de los SMA, la herramienta de inteligencia artificial (IA) más utilizada para el diseño de entornos virtuales inteligentes (IVE), los cuales son herramientas de simulación compleja basadas en agentes. Sin embargo, los IVE incorporan restricciones físicas (como gravedad, fuerzas, rozamientos, etc.), así como una representación 3D de lo que se quiere simular. Así mismo, estas herramientas no son sólo utilizadas para la realización de simulaciones. Con la aparición de nuevas aplicaciones como \emph{Internet of Things (IoT)}, \emph{Ambient Intelligence (AmI)}, robot asistentes, entre otras, las cuales están en contacto directo con el ser humano. Este contacto plantea nuevos retos a la hora de interactuar con estas aplicaciones. Una nueva forma de interacción que ha despertado un especial interés, es el que se relaciona con la detección y/o simulación de estados emocionales. Esto ha permitido que estas aplicaciones no sólo puedan detectar nuestros estados emocionales, sino que puedan simular y expresar sus propias emociones mejorando así la experiencia del usuario con dichas aplicaciones. Con el fin de mejorar la experiencia humano-máquina, esta tesis plantea como objetivo principal la creación de modelos emocionales sociales, los cuales podrán ser utilizados en aplicaciones MAS permitiendo a los agentes interpretar y/o emular diferentes estados emocionales y, además, emular fenómenos de contagio emocional. Estos modelos permitirán realizar simulaciones complejas basadas en emociones y aplicaciones más realistas en dominios como IoT, AIm, SH. / Over the past few years, multi-agent systems (SMA) have proven to be a powerful and versatile paradigm, with great potential for solving complex problems in dynamic and distributed environments. This potential is not primarily due to their individual characteristics (such as their autonomy, their capacity for perception, reaction and reasoning), but also the ability to communicate and cooperate in achieving a goal. In fact, its social capacity is the one that draws the most attention, it is this social behavior that gives potential to multi-agent systems. These characteristics have made the SMA, the artificial intelligence (AI) tool most used for the design of intelligent virtual environments (IVE), which are complex agent-based simulation tools. However, IVE incorporates physical constraints (such as gravity, forces, friction, etc.), as well as a 3D representation of what you want to simulate. Also, these tools are not only used for simulations. With the emergence of new applications such as \emph {Internet of Things (IoT)}, \emph {Ambient Intelligence (AmI)}, robot assistants, among others, which are in direct contact with humans. This contact poses new challenges when it comes to interacting with these applications. A new form of interaction that has aroused a special interest is that which is related to the detection and / or simulation of emotional states. This has allowed these applications not only to detect our emotional states, but also to simulate and express their own emotions, thus improving the user experience with those applications. In order to improve the human-machine experience, this thesis aims to create social emotional models, which can be used in MAS applications, allowing agents to interpret and / or emulate different emotional states, and emulate phenomena of emotional contagion. These models will allow complex simulations based on emotions and more realistic applications in domains like IoT, AIm, SH. / Al llarg dels últims anys, els sistemes multi-agent (SMA) han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexos en entorns dinàmics i distribuïts. Aquest potencial no es deu principalment a les seues característiques individuals (com són la seua autonomia, la seua capacitat de percepció, reacció i de raonament), sinó que també a la capacitat de comunicació i cooperació a l'hora d'aconseguir un objectiu. De fet, la seua capacitat social és la que més crida l'atenció, és aquest comportament social el que dota de potencial als sistemes multi-agent. Aquestes característiques han fet dels SMA, l'eina d'intel·ligència artificial (IA) més utilitzada per al disseny d'entorns virtuals intel·ligents (IVE), els quals són eines de simulació complexa basades en agents. No obstant això, els IVE incorporen restriccions físiques (com gravetat, forces, fregaments, etc.), així com una representació 3D del que es vol simular. Així mateix, aquestes eines no són només utilitzades per a la realització de simulacions. Amb l'aparició de noves aplicacions com \emph{Internet of Things (IOT)}, \emph{Ambient Intelligence (AmI)}, robot assistents, entre altres, les quals estan en contacte directe amb l'ésser humà. Aquest contacte planteja nous reptes a l'hora d'interactuar amb aquestes aplicacions. Una nova forma d'interacció que ha despertat un especial interès, és el que es relaciona amb la detecció i/o simulació d'estats emocionals. Això ha permès que aquestes aplicacions no només puguen detectar els nostres estats emocionals, sinó que puguen simular i expressar les seues pròpies emocions millorant així l'experiència de l'usuari amb aquestes aplicacions. Per tal de millorar l'experiència humà-màquina, aquesta tesi planteja com a objectiu principal la creació de models emocionals socials, els quals podran ser utilitzats en aplicacions MAS permetent als agents interpretar i/o emular diferents estats emocionals i, a més, emular fenòmens de contagi emocional. Aquests models permetran realitzar simulacions complexes basades en emocions i aplicacions més realistes en dominis com IoT, AIM, SH. / Rincón Arango, JA. (2018). Social Emotions in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/98090 / TESIS

<p>Food security is a major concern of human civilization. A way to ensure food security is to grow plants in a greenhouse under controlled conditions. Even under careful greenhouse production, stress in plants can emerge, and can cause damaging disease. To prevent yield loss farmers, apply resources, e.g., water, fertilizers, pesticides, higher/lower humidity, lighting, and temperature, uniformly in the infected areas. Research, however, shows that the practice leads to non-optimal profit and environmental protection.</p><p>Precision agriculture (PA) is an approach to address such challenges. It aims to apply the right amount or recourses at the right time and place. PA has been able to maximize crop yield while minimizing operation cost and environmental damage. The problem is how to obtain timely, precise information at each location to optimally treat the plants. There is scant research addressing strategies, algorithms, and protocols for analytics in PA. A monitoring and treating systems are the foci of this dissertation.</p><p>The designed systems comprise of agent- and system-level protocols and algorithms. There are four parts: (1) Collaborative Control Protocol for Cyber-Physical System (CCP-CPS); (2) Collaborative Control Protocol for Early Detection of Stress in Plants (CCP-ED); (3) Optimal Inspection Profit for Precision Agriculture; and (4) Multi-Agent System Optimization in Greenhouse for Treating Plants. CCP-CPS, a backbone of the system, establishes communication line among agents. CCP-ED optimizes the local workflow and interactions of agents. Next, the Adaptive Search algorithm, a key algorithm in CCP-ED, has analyzed to obtain the optimal procedure. Lastly, when stressed plants are detected, specific agents are dispatched to treat plants in a particular location with specific treatment. </p><p>Experimental results show that collaboration among agents statistically and significantly improves performance in terms of cost, efficiency, and robustness. CCP-CPS stabilizes system operations and significantly improves both robustness and responsiveness. CCP-ED enabling collaboration among local agents, significantly improves the number of infected plants found, and system efficiency. Also, the optimal Adaptive Search algorithm, which considers system errors and plant characteristics, significantly reduces the operation cost while improving performance. Finally, with collaboration among agents, the system can effectively perform a complex task that requires multiple agents, such as treating stressed plants with a significantly lower operation cost compared to the current practice.</p>