987 resultados para Decision-Maker
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This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.
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Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
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World Transport Policy & Practice, Vol.6, nº2, (2000)
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Based on the report for Project III of the PhD programme on Technology Assessment and prepared for the Winter School that took place at Universidade Nova de Lisboa, Caparica Campus on the 6th and 7th of December 2010.
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This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.
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The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.
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Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering
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Based on the report for “Project IV” unit of the PhD programme on Technology Assessment. This thesis research has the supervision of António Moniz (FCT-UNL and ITAS-KIT) and Manuel Laranja (ISEG-UTL). Other members of the thesis committee are Stefan Kuhlmann (Twente University), Leonhard Hennen (Karlsruhe Institute of Technology-ITAS), Tiago Santos Pereira (Universidade de Coimbra/CES) and Cristina Sousa (FCT-UNL).
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Based on the report for “Project IV” unit of the PhD programme on Technology Assessment (Doctoral Conference) at Universidade Nova de Lisboa (December 2011). This thesis research has the supervision of António Moniz (FCT-UNL and ITAS-KIT) and Michael Decker (Karlsruhe Institute of Technology-ITAS). Other members of the thesis committee are Carlos Alberto da Silva (University of Évora), José Maria de Albuquerque (Institute of Welding and Quality), Lotte Steuten (University of Twente), Mário Forjaz Secca (FCT-UNL) and Nelson Chibeles Martins (FCT-UNL).
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Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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BACKGROUND: This study's objective was to evaluate the role of psychological adjustment in the decision-making process to have an abortion and explore individual variables that might influence this decision. METHODS: In this cross-sectional study, we sequentially enrolled 150 women who made the decision to voluntarily terminate a pregnancy in Maternity Dr. Alfredo da Costa, in Lisbon, Portugal, between September 2008 and June 2009. The instruments were the Depression, Anxiety and Stress Scale (DASS), Satisfaction with Social Support Scale (SSSS), Emotional Assessment Scale (EAS), Decision Conflict Scale (DCS), and Beliefs and Values Questionnaire (BVQ). We analyzed the data using Student's T-tests, MANOVA, ANOVA, Tukey's post-hoc tests and CATPCA. Statistically significant effects were accepted for p<0.05. RESULTS: The participants found the decision difficult and emotionally demanding, although they also identified it as a low conflict decision. The prevailing emotions were sadness, fear and stress; but despite these feelings, the participants remained psychologically adjusted in the moment they decided to have an abortion. The resolution to terminate the pregnancy was essentially shared with supportive people and it was mostly motivated by socio-economic issues. The different beliefs and values found in this sample, and their possible associations are discussed. CONCLUSION: Despite high levels of stress, the women were psychologically adjusted at the time of making the decision to terminate the pregnancy. However, opposing what has been previously reported, the women presented high levels of sadness and fear, showing that this decision was hard to make, triggering disruptive emotions.
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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.
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Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Estudos Europeus
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Submitted to the graduate faculty Universidade Nova de Lisboa – Faculdade de Ciências e Tecnologia in partial fulfillment of the requirements for the degree of Master in Industrial Engineering