Dissertations / Theses on the topic 'Bayesian intelligence'
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Horsch, Michael C. "Dynamic Bayesian networks." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.
Full textScience, Faculty of
Computer Science, Department of
Graduate
Edgington, Padraic D. "Modular Bayesian filters." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3712276.
Full textIn this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filters. Since the time complexity of Bayesian filters is greater than linear, solving several smaller problems is cheaper than solving a single large problem. The cost of reassembling the results from the smaller problems is comparable to the cost of the smaller problems. This document introduces the concept of both exact and approximate modular Bayesian filters and describes how to design each of the elements of a modular Bayesian filters. These concepts are clarified by using a series of examples from the realm of vehicle state estimation and include the results of each stage of the algorithm creation in a simulated environment. A final section shows the implementation of a modular Bayesian filter in a real-world problem tasked with addressing the problem of vehicle state estimation in the face of transitory sensor failure. This section also includes all of the attending algorithms that allow the problem to be solved accurately and in real-time.
Hanif, A. "Computational intelligence sequential Monte Carlos for recursive Bayesian estimation." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1403732/.
Full textRoss, Stéphane. "Model-based Bayesian reinforcement learning in complex domains." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21960.
Full textL'apprentissage par renforcement a émergé comme une technique utile pour apprendre à accomplir une tâche de façon optimale à partir d'expérience dans les systèmes inconnus. L'un des problèmes majeurs de ces algorithmes d'apprentissage est comment balancer de façon optimale l'exploration du système, pour acquérir des connaissances, et l'exploitation des connaissances actuelles, pour compléter la tâche. L'apprentissage par renforcement bayésien avec modèle permet de résoudre ce problème de façon optimale en le formulant comme un problème de planification dans l'incertain. La complexité de telles méthodes a toutefois limité leur applicabilité à de petits domaines simples. Afin d'améliorer l'applicabilité de l'apprentissage par renforcement bayésian avec modèle, cette thèse presente plusieurs extensions de ces méthodes à des systèmes beaucoup plus complexes et réalistes, où le domaine est partiellement observable et/ou continu. Afin d'améliorer l'efficacité de l'apprentissage dans les gros systèmes, cette thèse inclue une autre extension qui permet d'apprendre automatiquement et d'exploiter la structure du système. Des algorithmes approximatifs sont proposés pour résoudre efficacement les problèmes d'inference et de planification résultants.
Gannon, Michael William. "Cruise missile proliferation : an application of Bayesian analysis to intelligence forecasting." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1992. http://handle.dtic.mil/100.2/ADA257717.
Full textThesis advisor: Edward J. Laurance. ADA257717. "September 1992". Includes bibliographical reference (p. 82-84).
Luo, Zhiyuan. "A probabilistic reasoning and learning system based on Bayesian belief networks." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1490.
Full textPomerantz, Daniel. "Designing a context dependant movie recommender: a hierarchical Bayesian approach." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86751.
Full textDans cette thèse, nous analysons un système de recommandations de films dépendant du contexte en utilisant un réseau Bayésien hiérarchique. Contrairement à la plupart des systèmes de recommendations qui, soit ne considère pas le contexte, soit le considère en utilisant le filtrage collaboratif, notre approche est basée sur le contenu. Ceci permet aux utilisateurs d'interpréter les contextes individuellement ou d'inventer leurs propres contextes tout en obtenant toujours de bonnes recommandations. En utilisant le rèseau Bayésien hiérarchique, nous pouvons fournir des recommendations en contexte quand les utilisateurs n'ont fourni que quelques informations par rapport à leurs préférences dans différents contextes. De plus, notre modèle a assez de degrés de liberté pour prendre en charge les utilisateurs avec des préférences différentes dans différents contextes. Nous démontrons sur un ensemble de données réel que l'utilisation d'un réseau Bayésien pour modéliser les contextes réduit l'erreur de validation croisée par rapport aux modèles qui ne lient pas les contextes ensemble ou qui ignore tout simplement le contexte.
Carr, S. "Investigating the applicability of bayesian networks to the analysis of military intelligence." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2826.
Full textJaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.
Full textSaini, Nishrith. "Using Machine Intelligence to Prioritise Code Review Requests." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20140.
Full textGurney, John. "Application of naïve Bayesian artificial intelligence to referral refinement of chronic open angle glaucoma." Thesis, Aston University, 2017. http://publications.aston.ac.uk/30341/.
Full textSuermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.
Full textRoussos, Evangelos. "Bayesian methods for sparse data decomposition and blind source separation." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589766.
Full textVural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.
Full textDissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
unrestricted
Ceccon, Stefano. "Extending Bayesian network models for mining and classification of glaucoma." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8051.
Full textGroves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.
Full textRey, Alexandre Del. "Inteligência dinâmica nas organizações: a utilização de redes bayesianas na redução de incertezas nos processos de inteligência competitiva." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-30032012-175423/.
Full textThe aim of this work is to explore Bayesian Networks as a tool to reduce uncertainties in the process of Competitive Intelligence. Here, by reviewing the concepts of Strategic Planning, Decision Making, Competitive Intelligence and the ability to infer of Bayesian Networks, it is proposed an approach for using these networks with this purpose. For this, a case study presents a step by step implementation of the proposed approach in a simulated management environment. In the case study, each step of the modeling scenarios is described in detail, emphasizing the care required for this modeling. With the modeling complete, two quasi-experiments were conducted in simulated environments to assess the perception and performance of decision makers who used Bayesian networks in comparison to the decision makers who have not used it. Data from the first quasi-experiment were not reliable and the second quasi-experiment did not form a representative sample from the statistical point of view. Nevertheless, it was possible to make contributions through the observations and data from these quasi-experiments conducted. From the standpoint of procedural, flaws in the construction of quasiexperiments and suggestions for improvement were presented. Regarding the tool modeled and constructed based on Bayesian Networks, it was possible to identify user perceptions regarding their use and suggestions for how to improve it. As for the performance data obtained, it was possible to examine in the second quasi-experiment, evidence, while not conclusive, that justify the new studies on the subject. Based on the literature and the evidence obtained, it is the possible that Bayesian Networks can be used for reducing uncertainty in the process of competitive intelligence and decisionmaking.
Yu, Shen. "A Bayesian machine learning system for recognizing group behaviour." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=32565.
Full textGopalakrishnan, Arjun. "Probabilistic Analysis of Contracting Ebola Virus Using Contextual Intelligence." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984182/.
Full textAbu-Hakmeh, Khaldoon Emad. "Assessing the use of voting methods to improve Bayesian network structure learning." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45826.
Full textDearden, Richard W. "Learning and planning in structured worlds." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0020/NQ56531.pdf.
Full textPieropan, Alessandro. "Structure learning of graphical models for task-oriented robot grasping." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amslaurea.unibo.it/1641/.
Full textAguiar, Sandra da Cruz Garcia do Espírito Santo. "Previsão do preço da Commodity do Butadieno a partir do uso de redes Bayesianas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/127232.
Full textThe theories that support pricing models have obtained little satisfactory or unsatisfactory results, once each study examines only one aspect of reality, without studying the problem as a whole. In this sense its necessary to explore under other aspects the dynamics of market variables that influence the pricing for its prior monitoring. The objective of this research was to build a decision support tool capable of periodically forecast the future price of a commodity in the short and medium term, especially for butadiene, an oil derivative. To make it possible, was done the dating of turning points in the price of this commodity compared to the historical events and based on these data to build this study on three structures: market, political and economic. Then, we identified the most consistent variables to form the basis of the research. The forecasts obtained show a higher performance compared to previous investigations. Thus, the forecast analysis of turning points is an informative tool to signal the future behavior of the price of this commodity. To understand the nature of these fluctuations, the method used in the pricing model were the Bayesian networks, which are capable of expressing the probabilities of a set of random variables defined previously and make appropriate predictions. The inference on the commodity price of butadiene – in the short and medium term, was performed using the Genie 2.0 software. The conclusion was that investing in research using artificial intelligence and predictive methods such as the Bayesian networks, has the advantage of understanding the relationship of cause and effect through scenario analysis. So the objective of building a decision support tool that can predict periodically, the price of butadiene in the short and medium term, has been achieved. For certain period was 84% accurate in forecasts of chances.
Karaaslan, Hatice. "A Study Of Argumentation In Turkish Within A Bayesian Reasoning Framework: Arguments From Ignorance." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614858/index.pdf.
Full textargument from ignorance&rdquo
or &ldquo
argumentum ad ignorantiam&rdquo
. The study was carried out in Turkish with Turkish participants. In the Bayesian framework, argument strength is determined by the interactions between three major factors: prior belief, polarity, and evidence reliability. In addition, topic effects are considered. Three experiments were conducted. The first experiment replicated Hahn et al.&rsquo
s (2005) study in Turkish to investigate whether similar results would be obtained in a different linguistic and cultural community. We found significant main effects of three of the manipulated factors in Oaksford and Hahn (2004) and Hahn et al. (2005): prior belief, reliability and topic. With respect to the Bayesian analysis, the overall fit between the data and the model was very good. The second experiment tested the hypothesis that argument acceptance would not vary across different intelligence levels. There was no significant main effect of prior belief, polarity, topic, and intelligence. We found a main effect of reliability only. However, further analyses on significant interactions showed that more intelligent subjects were less inclined to accept negative polarity items. Finally, the third experiment investigated the hypothesis that argument acceptance would vary depending on the presence of and the kind of evidentiality markers prevalent in Turkish, indicating the certainty with which events in the past have happened, marked with overt morpho-syntactic markers (&ndash
DI or &ndash
mIs). The experiment found a significant main effect of evidentiality as well as replicating the significant main effects of the two of the manipulated factors (prior belief and reliability) in Oaksford and Hahn (2004), Hahn et al. (2005) and in our first experiment. Furthermore, reliability and evidentiality interacted, indicating separate as well as combined effects of the two. With respect to the Bayesian analysis, the overall fit between the data and the model was lower than the one in the first experiment, but still acceptable. Overall, this study supported the normative Bayesian approach to studying argumentation in an interdisciplinary perspective, combining computation, psychology, linguistics, and philosophy.
Jambeiro, Filho Jorge Eduardo de Schoucair. "Tratamento bayesiano de interações entre atributos de alta cardinalidade." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276204.
Full textTese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Analisamos o uso de métodos Bayesianos em um problema de classificação de padrões de interesse prático para a Receita Federal do Brasil que é caracterizado pela presença de atributos de alta cardinalidade e pela existência de interações relevantes entre eles. Mostramos que a presença de atributos de alta cardinalidade pode facilmente gerar tantas subdivisões no conjunto de treinamento que, mesmo tendo originalmente uma grande quantidade de dados, acabemos obtendo probabilidades pouco confiáveis, inferidas a partir de poucos exemplos. Revisamos as estratégias usualmente adotadas para lidar com esse problema dentro do universo Bayesiano, exibindo sua dependência em suposições de não interação inaceitáveis em nosso domínio alvo. Mostramos empiricamente que estratégias Bayesianas mais avançadas para tratamento de atributos de alta cardinalidade, como pré-processamento para redução de cardinalidade e substituição de tabelas de probabilidades condicionais (CPTs) de redes Bayesianas (BNs) por tabelas default (DFs), árvores de decisão (DTs) e grafos de decisão (DGs) embora tragam benefícios pontuais não resultam em ganho de desempenho geral em nosso domínio alvo. Propomos um novo método Bayesiano de classificação, chamado de hierarchical pattern Bayes (HPB), que calcula probabilidades posteriores para as classes dado um padrão W combinando as observações de W no conjunto de treinamento com probabilidades prévias que são obtidas recursivamente a partir das observações de padrões estritamente mais genéricos que W. Com esta estratégia, ele consegue capturar interações entre atributos de alta cardinalidade quando há dados suficientes para tal, sem gerar probabilidades pouco confiáveis quando isso não ocorre. Mostramos empiricamente que, em nosso domínio alvo, o HPB traz benefícios significativos com relação a redes Bayesianas com estruturas populares como o naïve Bayes e o tree augmented naïve Bayes, com relação a redes Bayesianas (BNs) onde as tabelas de probabilidades condicionais foram substituídas pelo noisy-OR, por DFs, por DTs e por DGs, e com relação a BNs construídas, após uma fase de redução de cardinalidade usando o agglomerative information bottleneck. Além disso, explicamos como o HPB, pode substituir CPTs e mostramos com testes em outro problema de interesse prático que esta substituição pode trazer ganhos significativos. Por fim, com testes em vários conjuntos de dados públicos da UCI, mostramos que a utilidade do HPB ser bastante ampla
Abstract: In this work, we analyze the use of Bayesian methods in a pattern classification problem of practical interest for Brazil¿s Federal Revenue which is characterized by the presence of high cardinality attributes and by the existence of relevant interactions among them.We show that the presence of high cardinality attributes can easily produce so many subdivisions in the training set that, even having originally a great amount of data, we end up with unreliable probability estimates, inferred from small samples. We cover the most common strategies to deal with this problem within the Bayesian universe and show that they rely strongly on non interaction assumptions that are unacceptable in our target domain. We show empirically that more advanced strategies to handle high cardinality attributes like cardinality reduction by preprocessing and conditional probability tables replacement with default tables, decision trees and decision graphs, in spite of some restricted benefits, do not improve overall performance in our target domain. We propose a new Bayesian classification method, named hierarchical pattern Bayes (HPB), which calculates posterior class probabilities given a pattern W combining the observations of W in the training set with prior class probabilities that are obtained recursively from the observations of patterns that are strictly more generic than W. This way, it can capture interactions among high cardinality attributes when there is enough data, without producing unreliable probabilities when there is not. We show empirically that, in our target domain, HPB achieves significant performance improvements over Bayesian networks with popular structures like naïve Bayes and tree augmented naïve Bayes, over Bayesian networks where traditional conditional probability tables were substituted by noisy-OR gates, default tables, decision trees and decision graphs, and over Bayesian networks constructed after a cardinality reduction preprocessing phase using the agglomerative information bottleneck method. Moreover, we explain how HPB can replace conditional probability tables of Bayesian Networks and show, with tests in another practical problem, that such replacement can result in significant benefits. At last, with tests over several UCI datasets we show that HPB may have a quite wide applicability
Doutorado
Sistemas de Informação
Doutor em Ciência da Computação
Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.
Full textDepartment of Computing and Information Sciences
William Henry Hsu
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of documents spanning a period of time. It was later realized that these time-invariant models were not capable of modeling 1) the time varying number of topics they discover and 2) the time changing structure of these topics. Few models were developed to address this two deficiencies. The online-hierarchical Dirichlet process models the documents with a time varying number of topics. It varies the structure of the topics over time as well. However, it relies on document order, not timestamps to evolve the model over time. The continuous-time dynamic topic model evolves topic structure in continuous-time. However, it uses a fixed number of topics over time. In this dissertation, I present a model, the continuous-time infinite dynamic topic model, that combines the advantages of these two models 1) the online-hierarchical Dirichlet process, and 2) the continuous-time dynamic topic model. More specifically, the model I present is a probabilistic topic model that does the following: 1) it changes the number of topics over continuous time, and 2) it changes the topic structure over continuous-time. I compared the model I developed with the two other models with different setting values. The results obtained were favorable to my model and showed the need for having a model that has a continuous-time varying number of topics and topic structure.
Hernandes, André Carmona. "Análise de risco de colisão usando redes bayesianas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-05112013-162021/.
Full textThe safety of cars in traffic scenarios is being addressed on the past few years. One of its topics is the Advanced Driver-Assistance Systems which have been developed to reduce the fatality numbers of traffic accidents. These systems try to decrease human failures, such as imprudence and lack of attention while driving. For these reasons, the SENA project, in progress on the Mobile Robotics Laboratory at the Sao Carlos School of Engineering (EESC), aims to contribute for the evolution of these assistance systems. This work studies an artificial intelligence technique called Bayesian Networks. It deserves our attention due to its capability of handling uncertainties with probability distributions. The network developed in this Masters Thesis has, as input, the result of the classifiers used on SENA project and has, as output, a behavior which has to be performed by the vehicle with a driver or autonomously by the means of a path planner. Due to the high dimensionality of this issue, two different tests have been carried out. The first one was a braking experiment near a intersection point and the other one was a T-junction scenario. The tests made indicate that weak classifiers leaves the system more instable and error-prone and localization errors of the ego-vehicle have a stronger effect than just localization errors of other traffic participants. The experiments have shown that there is a necessity for a real-time system and a hardware more suitable to deal quickly with the information
Meganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.
Full textSuvorov, Anton. "Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7320.
Full textHowell, David R. "Finding needles in a haystack a resource allocation methodology to design strategies to detect terrorist weapon development /." Santa Monica, CA : RAND, 2009. http://www.rand.org/pubs/rgs_dissertations/2009/RAND_RGSD247.pdf.
Full text"This document was submitted as a dissertation in June 2009 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Gregory F. Treverton (Chair), Lynn E. Davis, David E. Mosher, and Walter L. Perry. Professor Kathryn Blackmond Laskey (George Mason University) was the external reader. Financial support for this dissertation was provided by RAND's National Defense Research Institute"--Cover. Title from title screen (viewed on Aug. 24, 2009). Includes bibliographical references: p. 100-105.
Azevedo, Carlos Renato Belo 1984. "Anticipation in multiple criteria decision-making under uncertainty = Antecipação na tomada de decisão com múltiplos critérios sob incerteza." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260775.
Full textTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treino
Abstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolume
Doutorado
Engenharia de Computação
Doutor em Engenharia Elétrica
Nogueira, Claudia Mendes. "Dificuldades orçamentárias básicas das famílias brasileiras: um convite à reflexão a partir de redes bayesianas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-03122012-193052/.
Full textThis study aims to understand the adequacy of Brazilians´ income to their needs and living conditions. According to the data from the Household Budget Survey (POF) conducted by IBGE (Brazilian Institute of Geography and Statistics) for the years of 2008 - 2009, the study identifies a model which focuses on the investigations about the fact that 75% of Brazilian households reported budgetary difficulties. To develop a model, was used the perceived adequacy of income declared by the householder or reference person in the household. The theoretical framework was based on consumer behavior and focuses on economic resources. The quantitative method was developed by Artificial Intelligence, specifically Bayesian Networks. Bayesian Networks are structures in the form of graphs for which the probability distributions are represented by nodes connected by acyclic arcs, which may or may not represent causal relationships between variables. At the end we intend to contribute to knowledge and improvement in the design of public policies and business in general, giving a more detailed look at what affects the difficulties of families, providing a vision that goes beyond the traditional division of economic classes.
Menart, Christopher J. Menart. "Global-Context Refinement for Semantic Image Segmentation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808.
Full textCoursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.
Full textOsunmakinde, Isaac Olusegun. "Computational intelligent systems : evolving dynamic Bayesian networks." Doctoral thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/6429.
Full textIncludes bibliographical references (p. 163-172).
In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are ideally suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the near future. The use of Dynamic Bayesian Networks in achieving situation awareness has been poorly explored in current research efforts. This research completely evolves DBNs automatically from any environment captured as multivariate time series (MTS) which minimizes the approximations and mitigates the challenges of choice of models. This potentially accommodates both highly skilled users and non-expert practitioners, and attracts diverse real-world application areas for DBNs. The architecture of our EDBN uses a combined strategy as it resolves two orthogonal issues to address the challenging problems: (1) evolving DBNs in the absence of domain experts and (2) mitigating computational intensity (or NP-hard) problems with economic scalability. Most notably, the major contributions of this thesis are as follows: the development of a new class of temporal probabilistic modeling (EDBN), whose architecture facilitates the demonstration of its emergent situation awareness (ESA) and emergent future situation awareness (EFSA) technologies. The ESA and its variant reveal hidden patterns over current and future time steps respectively. Among other contributions are the development and integration of an economic scalable framework called dynamic memory management in adaptive learning (DMMAL) into the architecture of the EDBN to emerge such network models from environments captured as massive datasets; the design of configurable agent actuators; adaptive operators; representative partitioning algorithms which facilitate the scalability framework; formal development and optimization of genetic algorithm (GA) to emerge optimal Bayesian networks from datasets, with emphasis on backtracking avoidance; and diverse applications of EDBN technologies such as business intelligence, revealing trends of insulin dose to medical patients, water quality management, project profitability analysis, sensor networks, etc.
Ben, Mrad Ali. "Observations probabilistes dans les réseaux bayésiens." Thesis, Valenciennes, 2015. http://www.theses.fr/2015VALE0018/document.
Full textIn a Bayesian network, evidence on a variable usually signifies that this variable is instantiated, meaning that the observer can affirm with certainty that the variable is in the signaled state. This thesis focuses on other types of evidence, often called uncertain evidence, which cannot be represented by the simple assignment of the variables. This thesis clarifies and studies different concepts of uncertain evidence in a Bayesian network and offers various applications of uncertain evidence in Bayesian networks.Firstly, we present a review of uncertain evidence in Bayesian networks in terms of terminology, definition, specification and propagation. It shows that the vocabulary is not clear and that some terms are used to represent different concepts.We identify three types of uncertain evidence in Bayesian networks and we propose the followingterminology: likelihood evidence, fixed probabilistic evidence and not-fixed probabilistic evidence. We define them and describe updating algorithms for the propagation of uncertain evidence. Finally, we propose several examples of the use of fixed probabilistic evidence in Bayesian networks. The first example concerns evidence on a subpopulation applied in the context of a geographical information system. The second example is an organization of agent encapsulated Bayesian networks that have to collaborate together to solve a problem. The third example concerns the transformation of evidence on continuous variables into fixed probabilistic evidence. The algorithm BN-IPFP-1 has been implemented and used on medical data from CHU Habib Bourguiba in Sfax
Medina, Oliva Gabriela. "Modélisation conjointe des connaissances multi-points de vue d'un système industriel et de son système de soutien pour l'évaluation des stratégies de maintenance." Thesis, Nancy 1, 2011. http://www.theses.fr/2011NAN10092/document.
Full textNowadays, the importance of the maintenance function has increased, due to the requirements on the maintain in operational conditions phase (MCO) of the system-of-interest (SI). As well as for the relevant role of maintenance in improving availability, performance efficiency, total plant availability, etc. To control performances, maintenance managers should be able to make some choices about the maintenance strategies and the resources that can fulfil the requirements. Within this context, we propose a methodology to formalize a model allowing to perform simulation to assess maintenance strategies. The scientific contribution of our work is that this approach unify by using a probabilistic relational model (PRM), different kind of knowledge needed to assess maintenance strategies. Knowledge is presented as generic and modular patterns based on PRM. These patterns integrate relevant decisional variables of the system of interest and of its maintenance system. This approach eases the modeling phase for a specific application. This methodology is one of the results of the project ANR SKOOB. This approach was tested on an industrial case for the maintenance of a harvest production process
Dumont, Julien. "Systèmes multi-agent pour le diagnostic pluri-disciplinaire." Phd thesis, Ecole Nationale Supérieure des Mines de Saint-Etienne, 2011. http://tel.archives-ouvertes.fr/tel-00844133.
Full textBen, Messaoud Montassar. "SemCaDo : une approche pour la découverte de connaissances fortuites et l'évolution ontologique." Phd thesis, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00716128.
Full textColes, Matthew David. "Bayesian network based intelligent mobility strategies for wireless sensor networks." Thesis, University of Portsmouth, 2009. https://researchportal.port.ac.uk/portal/en/theses/bayesian-network-based-intelligent-mobility-strategies-for-wireless-sensor-networks(23e8243c-d165-40c5-8838-7e8feaa8d965).html.
Full textGravem, Anne-Marit. "Integrating Case-based and Bayesian Reasoning for Decision Support." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11834.
Full textCampbell, Glenn G. "MediaHub, Bayesian Decision-making in an Intelligent Multimodal Distributed Platform Hub." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503741.
Full textSchlenoff, Craig. "Inferring intentions through state representations in cooperative human-robot environments." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS064/document.
Full textHumans and robots working safely and seamlessly together in a cooperative environment is one of the future goals of the robotics community. When humans and robots can work together in the same space, a whole class of tasks becomes amenable to automation, ranging from collaborative assembly to parts and material handling to delivery. Proposed standards exist for collaborative human-robot safety, but they focus on limiting the approach distances and contact forces between the human and the robot. These standards focus on reactive processes based only on current sensor readings. They do not consider future states or task-relevant information. A key enabler for human-robot safety in cooperative environments involves the field of intention recognition, in which the robot attempts to understand the intention of an agent (the human) by recognizing some or all of their actions to help predict the human’s future actions.We present an approach to inferring the intention of an agent in the environment via the recognition and representation of state information. This approach to intention recognition is different than many ontology-based intention recognition approaches in the literature as they primarily focus on activity (as opposed to state) recognition and then use a form of abduction to provide explanations for observations. We infer detailed state relationships using observations based on Region Connection Calculus 8 (RCC-8) and then infer the overall state relationships that are true at a given time. Once a sequence of state relationships has been determined, we use a Bayesian approach to associate those states with likely overall intentions to determine the next possible action (and associated state) that is likely to occur. We compare the output of the Intention Recognition Algorithm to those of an experiment involving human subjects attempting to recognize the same intentions in a manufacturing kitting domain. The results show that the Intention Recognition Algorithm, in almost every case, performed as good, if not better, than a human performing the same activity
Kim, Jong Hwan. "Autonomous Navigation, Perception and Probabilistic Fire Location for an Intelligent Firefighting Robot." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64997.
Full textPh. D.
Jackson, Arthur Rhydon. "Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning." Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etd/1820.
Full textQiu, Yumeng. "Leveraging Influential Factors into Bayesian Knowledge Tracing." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/53.
Full textSahin, Ferat. "A Bayesian Network Approach to the Self-organization and Learning in Intelligent Agents." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29034.
Full textPh. D.
Mora, Randall P., and Jerry L. Hill. "Service-Based Approach for Intelligent Agent Frameworks." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595661.
Full textThis paper describes a service-based Intelligent Agent (IA) approach for machine learning and data mining of distributed heterogeneous data streams. We focus on an open architecture framework that enables the programmer/analyst to build an IA suite for mining, examining and evaluating heterogeneous data for semantic representations, while iteratively building the probabilistic model in real-time to improve predictability. The Framework facilitates model development and evaluation while delivering the capability to tune machine learning algorithms and models to deliver increasingly favorable scores prior to production deployment. The IA Framework focuses on open standard interoperability, simplifying integration into existing environments.
Lin, Rongbin. "UAV Intelligent Path Planning for Wilderness Search and Rescue." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1759.
Full textHawkins, William J. "Boredom and student modeling in intelligent tutoring systems." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/307.
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