Este trabalho situa-se nas áreas de Sistemas Multiagentes e Geoprocessamento. Em Sistemas Multiagentes, a especificação de um sistema pode ser realizada através da modelagem do ambiente, agentes, mecanismos de interação e organização dos agentes envolvidos. Um aspecto pouco explorado, porém extremamente importante, é a modelagem e representação do ambiente em que os agentes estão situados e através do qual irão interagir. Por outro lado, o Geoprocessamento sempre enfatizou a representação de fenômenos espaciais de forma estática. No entanto, alguns fenômenos espaciais são inerentemente dinâmicos e as representações estáticas não os capturam de forma adequada. Assim, o foco principal deste trabalho é fornecer uma arquitetura, baseada em Sistemas Multiagentes, para a criação e execução de simulações na área de Geoprocessamento. Uma característica importante é a utilização de dados vetoriais, provenientes de um Banco de Dados Geográficos, na geração da representação espacial do ambiente e das entidades existentes nas simulações. Com esta forma de representação contínua e precisa, é possível criar modelos mais próximos da realidade, representando adequadamente um maior número de características. Além disto, a arquitetura permite a representação de fenômenos espaço-temporais dinâmicos, uma necessidade da área de Geoprocessamento. Focando no desenvolvimento das funcionalidades da arquitetura proposta, foi realizada uma análise das características positivas, negativas e das necessidades de algumas das principais plataformas para criação e execução de simulações baseadas em agentes. Considerando os resultados desta análise, foram criadas a arquitetura proposta e suas funcionalidades. Além disto, neste trabalho é apresentado o protótipo implementado, no qual foram realizados estudos de caso de diferentes cenários, objetivando avaliar e demonstrar o uso das funcionalidades desenvolvidas. / This work is situated in the intersection of two areas: Multi-Agent Systems and Geocomputing. In Multi-Agent Systems, the specification of a system can be achieved by the modelling of environment, agents, mechanisms of interaction and organization of the agents. Although being a very important aspect, the modelling and representation of the environment has not been yet fully explored. On the other hand, Geocomputing has always focused in the static representation of spatial phenomena. However, some spatial phenomena are dynamic on time and space and usual representation adopted in Geocomputing do not capture them well. The aim of this work is to provide an architecture, based on Multi-Agent Systems, for the development and execution of Geocomputing simulations. A very important feature of the proposed architecture is the use of vectorial data, from a Geographic Database, for the representation of the environment and the spatial entities that exist in it. Using this continuous and accurate representation form, it is possible to develop more realistic models, representing appropriately geographic features. Moreover, this architecture allows the representation of dynamic phenomena in time and space, an old need of the Geocomputing area. Focusing on the development of the features of this architecture, an analysis of some related works was realized. The development of this architecture and its features was done based on the results of this analysis. Moreover, a prototype was presented in this work, in which some case studies of different scenes were performed, aiming at the evaluation and demonstration of the developed model and its features.

A análise da informação contida em seqüências genéticas tem ganho cada vez mais importância nos dias atuais. A chamada anotação genética tem o objetivo de, a partir de uma ou mais seqüências, determinar suas características estruturais e funcionais. Muitos processos de anotação já foram realizados com êxito aumentando consideravelmente nosso conhecimento acerca do mecanismo genético de diversos organismos. A re-anotação genética é um processo que visa revisar o resultado da anotação, em virtude da disponibilidade de novas informações. Neste trabalho foi desenvolvido um sistema de re-anotação automática, onde tarefas de análise repetitivas podem ser automatizadas e os dados na anotação re-analisados periodicamente, a fim de que possíveis modificações possam ser detectadas. O sistema é baseado na tecnologia de agentes. Cada agente é responsável pela execução de diferentes ferramentas de bioinformática. Ao final do processo, os resultados individuais são combinados a fim de atingir o objetivo da análise. O sistema demonstrou eficácia na análise realizada em organismos procariontes durante a fase de validação. Ambientes de re-anotação como este são ferramentas interessantes a serem futuramente integradas a sistemas de anotação existentes. / The analysis of the information present in genetic sequences is gaining more importance nowadays. The genetic annotation aims to determine structural and functional characteristics of the sequences. Many annotation processes have already been carried out, improving our knowledge about the genetic mechanism of several organisms. The genetic re-annotation is a process that aims to review the annotation results when new information is available. This work presents an automatic re-annotation system where repetitive analysis tasks can be automated and annotation data are periodically re-analysed in order to detect possible differences. The system is based on the agent technology and each agent must execute different bioinformatics tools and merge its results in order to reach the analysis goals. The system proved to be efficient on the analysis carried out in procariotic organisms in the validation process, becoming an interesting tool to be integrated in annotation systems in the future.

O planejamento urbano é muito importante para garantir o desenvolvimento sustentável das cidades modernas. Para isso, muitos aspectos devem ser considerados, tais como o crescimento urbano das mesmas, uma vez que, o entendimento das dinâmicas desse fenômeno é um pré-requisito para modelar e prever tendências futuras de mudanças de uso do solo/cobertura da terra (LUCC) e seus impactos ecológicos. Como as cidades são sistemas complexos, que envolvem vários atores com diferentes padrões de comportamento, uma das mais promissoras classes de modelos urbanos designados para simular e analisar mudanças de uso do solo são as baseadas em Sistemas Multiagentes (SMA). Sendo assim, este trabalho descreve uma proposta de abordagem baseada em agentes reativos aplicados na tarefa de simulação de crescimento urbano para a cidade de Bauru/SP, utilizando dados geográficos, extraídos de imagens de satélite, da ocupação e utilização efetiva do uso do solo dessa cidade em um período de aproximadamente trinta e cinco anos, para calibrar e validar o simulador. / Urban planning is a very important issue to guarantee the sustainable development of modern cities. For this, many aspects must be considered such as the urban growth. Understanding the dynamics of this phenomenon is a prerequisite for modelling and forecasting future trends of urban land use/cover change (LUCC) and its ecological impacts. Cities being complex systems, which involve various actors with different patterns of behaviour, one of the most promising class of urban models designed to simulate and analyze LUCC are based in Multi-Agent System (MAS). Hence, this work describes a proposal based in reactive agents applied in the simulation of urban growth of the city of Bauru/SP, using geographic data, extracted from satellite images, of the occupation and land use in this city in a time span of approximately thirty five years, to calibrate and validate the simulator developed.

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.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-13T15:08:25Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_FernandoRocha.pdf: 2031602 bytes, checksum: c22be1291e0dd7d53360e8930c5f5927 (MD5) / Made available in DSpace on 2017-02-13T15:08:25Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_FernandoRocha.pdf: 2031602 bytes, checksum: c22be1291e0dd7d53360e8930c5f5927 (MD5) Previous issue date: 2015-03-13 / CAPES / O desenvolvimento de soluções de Inteligência Artificial (IA) para sistemas computacionais é complexo dado a natureza dos problemas atacados, em particular quando envolvem problemas multiagentes e multitarefas (MAMT). Apesar de existirem vários métodos para o desenvolvimento de Sistema Multiagentes (SMA), são poucos os que dão alguma importância à compreensão do problema; e mesmo estes métodos não abordam os problemas MAMT com o devido detalhamento. Abordando a deficiência destas metodologias, estamos propondo o método Icelus que foca em guiar o analista em compreender e descrever corretamente o problema a ser solucionado. Icelus permitirá uma melhor abordagem na análise e compreensão de um problema MAMT, facilitando a distribuição do conhecimento para o restante do time de desenvolvimento, reduzindo o risco de erros de codificação ao longo do desenvolvimento do projeto. / The development of Artificial Intelligence (AI) to computational systems is a complex activity, given the nature of the problems attacked, in particular when they involve multi-agent problems and multitasking (MAMT). Although there are several methods for the development of Multi-agent System (MAS), there are just a few that give any importance to understanding the problem; and even these methods do not address the problems with all detailing that MAMT problems needs. Addressing the deficiency of these methods, we are proposing the Icelus method that focuses on leading the analyst to understand and describe correctly the problem to be solved. Icelus will enable a better approach in the analysis and understanding of a MAMT problem, facilitating the distribution of knowledge to the rest of the development team, reducing the risk of coding errors throughout the development of the project.

Flocking birds in a virtual world through adaption of Craig Reynolds, Boids algorithm. In this paper I will demonstrate how I adapted the Boids algorithm to an already existing code to create a flocking behaviour and discuss options to the solutions. I have been given a virtual world with existing birds that is moving around based on random elements that in a way mimics flying.

Paralleling the rapid advancement in the network evolution is the need for advanced network traffic management surveillance. The increasing number and variety of services being offered by communication networks has fuelled the demand for optimized load management strategies. The problem of Load Control Management in Intelligent Networks has been studied previously and four Multi-Agent architectures have been proposed. The objective of this thesis is to investigate one of the quality attributes namely, scalability of the four Multi-Agent architectures. The focus of this research would be to resize the network and study the performance of the different architectures in terms of Load Control Management through different scalability attributes. The analysis has been based on experimentation through simulations. It has been revealed through the results that different architectures exhibit different performance behaviors for various scalability attributes at different network sizes. It has been observed that there exists a trade-off in different scalability attributes as the network grows. The factors affecting the network performance at different network settings have been observed. Based on the results from this study it would be easier to design similar networks for optimal performance by controlling the influencing factors and considering the trade-offs involved. / C/o Aijaz Ahmad, House No. E-97/A, Street No. 4, Super Town, Walton Road, Lahore Cantt.-Pakistan, Tel +92-42-6655070

In this thesis we presented a new direction for handling missing values in multi agent-based simulation (MABS) at micro-level by using truth tables and logical relations. Although micro-level simulation is a vast field to use logical relations with truth tables to find missing values but it takes values into account at individual levels. We used databases in form of tables to extract missing values. Our literature review suggested us a method for input handling by using electronically saved truth tables. We have defined logical relations according to scenario by interacting with truth tables to find appropriate missing values. Our conclusions suggested a method which can find appropriate values for input parameters when they are missing. Accurate results have been gained according to updated database. In this thesis we have concluded that missing values would be handled in different ways, such as: Artificial neural network, K-nearest neighbor, Statistical method and Data mining; etc… These methods have not facilitated in finding appropriate missing values as we saw in literature. We have created a method that can find missing values and produce good results. We have run our method on a specific scenario to check the efficiency of input handling that motivated us to arrange database in a proper way to handle missing values along.

The study of Power TAC is a Multi-Agent competitive simulation test-bed, providing opportunity to simulate research and developments of electronic agents which can manage the tasks of the consumers and energy resources in a virtual energy infrastructure. According to the Power TAC scenario, Plug-in Electrical Vehicles are a special type of consumers that interact with this infrastructure and sometimes with the producers through aggregators. The aim of this study is modeling an intelligent Plug-in Electric vehicle agent for Power TAC that acts as an intermediary between Power TAC grid and vehicle owners. The proposed agent acts autonomously and is capable of making decisions about its energy needs by learning the driving behaviors and other preferences of these vehicle owners in a specified time interval. These agents will be able to make decisions about buying energy from the grid when the charging process is necessary or sell their energy back to the grid when the conditions of the electricity market are sufficiently attractive. The objective of this study is to model a Multi-Agent system for automating the process of charging the plug-in Vehicle Agents in Power TAC scenario by determining the necessary agents and the simulation environment where the agents constructed and simulated. Аs results of this study, different strategies are defined by considering the preferences of the vehicle owners and the conditions of the vehicle; thereby the agents autonomously bid behalf of their user in order to automate the process of charging.

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.