903 resultados para seleção criteriosa
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Dissertação de Mestrado em Gestão de Empresas/MBA
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Tese de Doutoramento, Ciências do Mar (Biologia Marinha)
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Trabalho Final de mestrado para obtenção do grau de Mestre em Engenharia Civil
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Dissertação apresentada para obtenção do grau de Mestre em Ciências da Educação Área de especialização em Intervenção Precoce
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Dissertação de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica Ramo de Manutenção e Produção
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes
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Dissertação de Mestrado, Ciências Biomédicas, 18 de Novembro de 2015, Universidade dos Açores.
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Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.
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Master Thesis in Mechanical Engineering field of Maintenance and Production
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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística, na Especialização de Artes Plásticas na Educação
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil Especialização em Edificações
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Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino de 1.º e 2.º Ciclos do Ensino Básico