O presente trabalho emprega Redes Bayesianas para a previsão da rentabilidade de empresas. Define-se como rentabilidade superior as empresa que obtiveram retorno para os acionistas classificados acima de 81,5% em relação às demais. Adota-se a metodologia de seleção dos indicadores proposta por Sun e Shenoy (2007), que seleciona as variáveis explicativas segundo suas correlações com a variável classificadora. Obtêm-se, ao final, dois modelos sendo o primeiro com dois estados de classificação de empresas, superior e inferior; o segundo com três estados (superior mediano e inferior). Assim como Sun e Shenoy (2007), tenta-se validar o modelo Bayesiano com a regressão logística. Constata-se que não é possível afirmar que as média das taxas de sucesso dos dois modelos sejam diferentes ao se prever rentabilidade superior, entretanto a regressão tem melhor desempenho ao se prever rentabilidade baixa. A variável mais significativa tanto para o primeiro quanto para o segundo modelos foi a classificação atual da empresa, ou seja, empresas que figuram em um determinado ano no estado de rentabilidade superior são as mais propensas a repetir o resultado do que as demais. Os resultados apontam taxas de acerto que vão de 14,70% em 1999 (ano da crise cambial quando a rentabilidade média das empresas foi de 2,74%) a 52,94% em 1997 (ano cuja rentabilidade média foi de 11,76%) para o primeiro modelo e de 11,76 % (1999) a 56,60 % (2004, rentabilidade média de 10,76%) para o segundo modelo. Apesar dos modelos ainda não conseguirem alcançar uma estabilidade nas previsões os resultados são animadores quando se desenvolve a hipótese de utilidade para um possível investidor e a expectativa de retorno acumulado, ao longo dos dez anos, passa de 70,37%, que é a rentabilidade média acumulada do período, para 357,07% e 410,10 % para o primeiro e o segundo modelo respectivamente. / This work use the knowledge obtained from Bayesian networks studies of bankruptcy prediction and applied it for forecasting companies' profitability. Higher profitability is defined as the company that had returns for shareholders classified over 81.5% compared to the others. Adopting the methodology of selection of the explanatory variables proposed by Sun and SHENOY (2007) based on correlations among them with the classification variable. As a result it is obtained two models, the first one with two classification states for de classification variable, upper and low, and the second one with three states (upper, middle and low). As Sun and SHENOY (2007), the Bayesian model was compared with a logistic regression. It cannot be say that the average success rates of the two models are different for forecasting higher profitability; otherwise, for low profitability forecasts the regression model was superior. The most significant variable for both the first and for the second model was the previous company's return for the shareholders, i.e. companies that are in a given year in the state of upper profitability are more likely to repeat the resulting the next year. The results show success rates ranging from 14.70% in 1999 (year of the currency crisis when the average profitability of the companies was 2.74%) to 52.94% in 1997 (average return rate was 11.76 %) for the first model and from 11.76% (1999) to 56.60% (2004, average return rate was 10.76%) for the second model. Although the models still fail to achieve stability in the estimates the results are encouraging when developing the hypothesis of possible investor profitability when the expectation of return accumulated over the ten years, range from 70.37%, which is the average profitability accumulated in the period to 357.07% and 410.10% respectively for the first and second model.

abstract: Bayesian networks are powerful tools in system reliability assessment due to their flexibility in modeling the reliability structure of complex systems. This dissertation develops Bayesian network models for system reliability analysis through the use of Bayesian inference techniques. Bayesian networks generalize fault trees by allowing components and subsystems to be related by conditional probabilities instead of deterministic relationships; thus, they provide analytical advantages to the situation when the failure structure is not well understood, especially during the product design stage. In order to tackle this problem, one needs to utilize auxiliary information such as the reliability information from similar products and domain expertise. For this purpose, a Bayesian network approach is proposed to incorporate data from functional analysis and parent products. The functions with low reliability and their impact on other functions in the network are identified, so that design changes can be suggested for system reliability improvement. A complex system does not necessarily have all components being monitored at the same time, causing another challenge in the reliability assessment problem. Sometimes there are a limited number of sensors deployed in the system to monitor the states of some components or subsystems, but not all of them. Data simultaneously collected from multiple sensors on the same system are analyzed using a Bayesian network approach, and the conditional probabilities of the network are estimated by combining failure information and expert opinions at both system and component levels. Several data scenarios with discrete, continuous and hybrid data (both discrete and continuous data) are analyzed. Posterior distributions of the reliability parameters of the system and components are assessed using simultaneous data. Finally, a Bayesian framework is proposed to incorporate different sources of prior information and reconcile these different sources, including expert opinions and component information, in order to form a prior distribution for the system. Incorporating expert opinion in the form of pseudo-observations substantially simplifies statistical modeling, as opposed to the pooling techniques and supra Bayesian methods used for combining prior distributions in the literature. The methods proposed are demonstrated with several case studies. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

Individualmente, as linhas de pesquisa da Inteligência Artificial têm proposto abordagens para a resolução de inúmeros problemas complexos do mundo real. O paradigma orientado a agentes provê os agentes autônomos, capazes de perceber os seus ambientes, reagir de acordo com diferentes circunstâncias e estabelecer interações sociais com outros agentes de software ou humanos. As redes Bayesianas fornecem uma maneira de representar graficamente as distribuições de probabilidades condicionais e permitem a realização de raciocínios probabilísticos baseados em evidências. As ontologias são especificações explícitas e formais de conceituações, que são usadas em uma variedade de áreas de pesquisa, incluindo os Sistemas Multiagentes. Contudo, existem aplicações cujos requisitos não podem ser atendidos por uma única tecnologia. Circunstâncias como estas exigem a integração de tecnologias desenvolvidas por distintas áreas da Ciência da Computação. Esta dissertação trata a integração do modelo de agentes BDI (Belief-Desire-Intention) e das redes Bayesianas. Além disso, é adotada uma abordagem baseada em ontologias para representar o conhecimento incerto dos agentes. O primeiro passo em direção a integração foi o desenvolvimento de uma ontologia para representar a estrutura das redes Bayesinas. Esta ontologia tem como principal objetivo permitir a interoperabilidade agentes compatíveis com a arquitetura proposta. No entanto, a ontologia também facilita o entendimento necessário para abstrair os estados mentais e processos cognitivos dos agentes através de elementos das redes Bayesianas. Uma vez construída a ontologia, a mesma foi integrada com a arquitetura BDI. Através da integração do modelo BDI com as redes Bayesianas foi obtida uma arquitetura cognitiva de agentes capaz de deliberar sob incerteza. O processo de integração foi composto de duas etapas: abstração dos estados mentais através de elementos das redes Bayesianas e especificação do processo deliberativo. Finalmente, foi desenvolvido um estudo de caso, que consistiu na aplicação da arquitetura proposta no Agente Social, um componente de um portal educacional multiagente (PortEdu). / Individually, Artificial Intelligence research areas have proposed approaches to solve several complex real-world problems. The agent-based paradigm provided autonomous agents, capable of perceiving their environment, reacting in accordance with different situations, and establishing social interactions with other software agents and humans. Bayesian networks provided a way to represent graphically the conditional probability distributions and an evidence-based probabilistic reasoning. Ontologies are an effort to develop formal and explicit specifications of concepts, which have been used by a wide range of research areas, including Multiagent Systems. However, there are applications whose requirements can not be addressed by a single technology. Circumstances like these demand the integration of technologies developed by distinct areas of Computer Science. This work is particularly concerned with the integration of Belief-Desire-Intention (BDI) agent architecture and Bayesian networks. Moreover, it is adopted an ontology-based approach to represent the agent’s uncertain knowledge. To bring together those technologies, it was developed an ontology to represent the structure of Bayesian networks knowledge representation. This ontology supports the interoperability among agents that comply with the proposed architecture, and it also facilitates the understanding necessary to abstract the agents’ mental states and cognitive processes through elements of Bayesian networks. Once specified the ontology, it was integrated with the BDI agent architecture. By integrating BDI architecture and Bayesian networks, it was obtained a cognitive agent architecture capable of reasoning under uncertainty. It was performed in two stages: abstraction of mental states through Bayesian networks and specification of the deliberative process. Finally, it was developed a case study, which consists in applying the probabilistic BDI architecture in the Social Agent, a component of a multiagent educational portal (PortEdu).

Individualmente, as linhas de pesquisa da Inteligência Artificial têm proposto abordagens para a resolução de inúmeros problemas complexos do mundo real. O paradigma orientado a agentes provê os agentes autônomos, capazes de perceber os seus ambientes, reagir de acordo com diferentes circunstâncias e estabelecer interações sociais com outros agentes de software ou humanos. As redes Bayesianas fornecem uma maneira de representar graficamente as distribuições de probabilidades condicionais e permitem a realização de raciocínios probabilísticos baseados em evidências. As ontologias são especificações explícitas e formais de conceituações, que são usadas em uma variedade de áreas de pesquisa, incluindo os Sistemas Multiagentes. Contudo, existem aplicações cujos requisitos não podem ser atendidos por uma única tecnologia. Circunstâncias como estas exigem a integração de tecnologias desenvolvidas por distintas áreas da Ciência da Computação. Esta dissertação trata a integração do modelo de agentes BDI (Belief-Desire-Intention) e das redes Bayesianas. Além disso, é adotada uma abordagem baseada em ontologias para representar o conhecimento incerto dos agentes. O primeiro passo em direção a integração foi o desenvolvimento de uma ontologia para representar a estrutura das redes Bayesinas. Esta ontologia tem como principal objetivo permitir a interoperabilidade agentes compatíveis com a arquitetura proposta. No entanto, a ontologia também facilita o entendimento necessário para abstrair os estados mentais e processos cognitivos dos agentes através de elementos das redes Bayesianas. Uma vez construída a ontologia, a mesma foi integrada com a arquitetura BDI. Através da integração do modelo BDI com as redes Bayesianas foi obtida uma arquitetura cognitiva de agentes capaz de deliberar sob incerteza. O processo de integração foi composto de duas etapas: abstração dos estados mentais através de elementos das redes Bayesianas e especificação do processo deliberativo. Finalmente, foi desenvolvido um estudo de caso, que consistiu na aplicação da arquitetura proposta no Agente Social, um componente de um portal educacional multiagente (PortEdu). / Individually, Artificial Intelligence research areas have proposed approaches to solve several complex real-world problems. The agent-based paradigm provided autonomous agents, capable of perceiving their environment, reacting in accordance with different situations, and establishing social interactions with other software agents and humans. Bayesian networks provided a way to represent graphically the conditional probability distributions and an evidence-based probabilistic reasoning. Ontologies are an effort to develop formal and explicit specifications of concepts, which have been used by a wide range of research areas, including Multiagent Systems. However, there are applications whose requirements can not be addressed by a single technology. Circumstances like these demand the integration of technologies developed by distinct areas of Computer Science. This work is particularly concerned with the integration of Belief-Desire-Intention (BDI) agent architecture and Bayesian networks. Moreover, it is adopted an ontology-based approach to represent the agent’s uncertain knowledge. To bring together those technologies, it was developed an ontology to represent the structure of Bayesian networks knowledge representation. This ontology supports the interoperability among agents that comply with the proposed architecture, and it also facilitates the understanding necessary to abstract the agents’ mental states and cognitive processes through elements of Bayesian networks. Once specified the ontology, it was integrated with the BDI agent architecture. By integrating BDI architecture and Bayesian networks, it was obtained a cognitive agent architecture capable of reasoning under uncertainty. It was performed in two stages: abstraction of mental states through Bayesian networks and specification of the deliberative process. Finally, it was developed a case study, which consists in applying the probabilistic BDI architecture in the Social Agent, a component of a multiagent educational portal (PortEdu).

O presente trabalho emprega Redes Bayesianas para a previsão da rentabilidade de empresas. Define-se como rentabilidade superior as empresa que obtiveram retorno para os acionistas classificados acima de 81,5% em relação às demais. Adota-se a metodologia de seleção dos indicadores proposta por Sun e Shenoy (2007), que seleciona as variáveis explicativas segundo suas correlações com a variável classificadora. Obtêm-se, ao final, dois modelos sendo o primeiro com dois estados de classificação de empresas, superior e inferior; o segundo com três estados (superior mediano e inferior). Assim como Sun e Shenoy (2007), tenta-se validar o modelo Bayesiano com a regressão logística. Constata-se que não é possível afirmar que as média das taxas de sucesso dos dois modelos sejam diferentes ao se prever rentabilidade superior, entretanto a regressão tem melhor desempenho ao se prever rentabilidade baixa. A variável mais significativa tanto para o primeiro quanto para o segundo modelos foi a classificação atual da empresa, ou seja, empresas que figuram em um determinado ano no estado de rentabilidade superior são as mais propensas a repetir o resultado do que as demais. Os resultados apontam taxas de acerto que vão de 14,70% em 1999 (ano da crise cambial quando a rentabilidade média das empresas foi de 2,74%) a 52,94% em 1997 (ano cuja rentabilidade média foi de 11,76%) para o primeiro modelo e de 11,76 % (1999) a 56,60 % (2004, rentabilidade média de 10,76%) para o segundo modelo. Apesar dos modelos ainda não conseguirem alcançar uma estabilidade nas previsões os resultados são animadores quando se desenvolve a hipótese de utilidade para um possível investidor e a expectativa de retorno acumulado, ao longo dos dez anos, passa de 70,37%, que é a rentabilidade média acumulada do período, para 357,07% e 410,10 % para o primeiro e o segundo modelo respectivamente. / This work use the knowledge obtained from Bayesian networks studies of bankruptcy prediction and applied it for forecasting companies' profitability. Higher profitability is defined as the company that had returns for shareholders classified over 81.5% compared to the others. Adopting the methodology of selection of the explanatory variables proposed by Sun and SHENOY (2007) based on correlations among them with the classification variable. As a result it is obtained two models, the first one with two classification states for de classification variable, upper and low, and the second one with three states (upper, middle and low). As Sun and SHENOY (2007), the Bayesian model was compared with a logistic regression. It cannot be say that the average success rates of the two models are different for forecasting higher profitability; otherwise, for low profitability forecasts the regression model was superior. The most significant variable for both the first and for the second model was the previous company's return for the shareholders, i.e. companies that are in a given year in the state of upper profitability are more likely to repeat the resulting the next year. The results show success rates ranging from 14.70% in 1999 (year of the currency crisis when the average profitability of the companies was 2.74%) to 52.94% in 1997 (average return rate was 11.76 %) for the first model and from 11.76% (1999) to 56.60% (2004, average return rate was 10.76%) for the second model. Although the models still fail to achieve stability in the estimates the results are encouraging when developing the hypothesis of possible investor profitability when the expectation of return accumulated over the ten years, range from 70.37%, which is the average profitability accumulated in the period to 357.07% and 410.10% respectively for the first and second model.

Analyse et conception de la fiabilité sont indispensables pour le processus de développement des systèmes mécatroniques. Toutefois, des outils puissants sont nécessaires en raison de la complexité croissante et de la cherté d'essai des systèmes mécatroniques. Cette complexité nous amène des difficultés de l'incertitude de modélisation et de la dépendance inconnus, tels que la dépendance fonctionnelle et temporelle. Pour faire face à une telle complexité, la fiabilité des outils d'analyse doivent être mathématiquement puissant, facile à utiliser et efficace de calcul.Les outils classiques ont une certaine quantité d'inconvénients lors de l'évaluation de la fiabilité au niveau du système. Par exemple, les méthodes basées sur la chaîne de Markov ont un problème infime d'explosion combinatoire et le formalisme de l'arbre de défaillance ne fonctionne que quand les composants sont indépendants les uns des autres. Bien que certaines extensions, comme les arbres de défaillance dynamiques, aient été faites pour pallier les lacunes, tous ne peuvent être traitées dans un cadre unique. Le formalisme des réseaux Bayésiens a été récemment considéré comme un outil prometteur de l'inférence statistique pour l'évaluation de fiabilité du système grâce à de nombreux avantages, tels que la capacité de modélisation de la dépendance incertaine, l'intégration de données provenant de diverses sources et les outils de raisonnement bien étudiés. D'autre part, la plus grande valeur ajoutée en mécatronique est en sous-système du contrôle et du traitement de l'information. Les ingénieurs se rendent compte que la conception de contrôleur d'un système dynamique ne peut pas négliger l'exigence de la fiabilité dynamique. Diverses incertitudes influencent non seulement les performances des contrôleurs, mais aussi la fiabilité. Cependant, peu de recherches ont examiné la fiabilité dynamique des contrôleurs.Dans cette recherche, nous avons étudié le formalisme des réseaux bayésiens et développé une méthode de l'évaluation de la fiabilité des systèmes mécatroniques complexes. Cette méthode étend l'analyse bayésienne sur les composants à celle sur les systèmes complexes et permet de considérer des incertitudes des paramètres des modèles asymétriques de temps à l'échec dans les systèmes complexes. Pour effectuer l'inférence dans notre modèle de réseau bayésien, nous avons développé un algorithme modifié de la propagation de croyances non-paramétrique qui est plus efficace dans le cas complexe par rapport à d'autres outils de raisonnement. Nous avons montré également comment effectuer l'analyse de sensibilité dans notre modèle de réseau bayésien qui a une structure non-déterministe.Un contrôleur linéaire dynamique-fiable a été conu pour le module de contrôle des systèmes mécatroniques. Nous avons établi un nouveau lien entre le probabilité de la défaillance du premier passage et les gains de rétroaction des contrôleurs, et obtenu une nouvelle contrainte dynamique de fiabilité pour les objectifs classiques. Le contrôleur linéaire dynamique-fiable est également étendu au formalisme de multiple-modèle pour que la réalisation d'un contrôleur dynamique-fiable soit applicable dans le cas nonlinéaire/non-gaussien. La performance du système peut encore être améliorée dans ce cadre en utilisant les méthodes de multiple-modèle plus avancées.Une grande quantité de résultats de simulation ont démontré que les méthodes développées ont été appliquées avec succès pour analyser et concevoir des systèmes de suspension active du véhicule et peuvent être appliquée à d'autres applications, telles que d'autres systèmes mécatroniques et systèmes de contrôle actif de construction. / Reliability analysis and design become indispensable for the development process of mechatronic systems. However, versatile tools are called for because of the increas•ing complexity and the testing expensiveness of mechatronic systems. Such complexity brings the difficulties of modeling uncertainty and unknown dependency, such as functional and temporal dependency. To deal with such complexity, reliability analysis tools need to be mathematically powerful, be easy to use and be computationally efficient.Conventional tools have a number of drawbacks when evaluating the reliability at system level. For instance, Markov chain based methods have a problem of infamous combinatorial explosion and fault trees formalism works under the assumption of component independency. Although sorne extensions, such as dynamic fault trees, have been made to make up for shortcomings, not all of them can be handled in one framework. Bayesian networks formalism is recently believed to be a promising statistical inference tool for system reliability assessment thanks to many advantages, such as the ability of modeling uncertain dependency, integrating data from diverse sources and the well-studied reasoning tools. On the other hand, the biggest value-added in mechatronics is in control/information processing subsystem. Engineers realize that the controller design of a dynamic system cannot neglect the dynamic reliability requirement. Various uncertainties influence not only the controller performance but also the reliability. However, little research has considered the dynamic reliability of controllers.In this research, we have investigated the Bayesian networks formalism and developed a new system reliability assessment method for complex mechatronic systems. This method extends Bayesian analysis on components to that on complex systems and allows to consider parameter uncertainties of various skewed time-to-failure models in complex systems. To perform the inference in our Bayesian network model, we developed a modified nonparametric beHef propagation which is more efficient in the complex case compared with other reasoning tools. We showed also how to perform the sensitivity analysis in our Bayesian network model that has a non-deterministic structure. A dynamic-reliable linear controller has been designed for the control module of mechatronic systems. We established a new link between the first-passage failure probability and controllers' feedback gains, and obtained a new dynamic-reliability constraint for classical objectives. The dynamic-reliable linear controller is also extended to the multiple model formalism for achieving a dynamic-reliable controller applicable to nonlinearjnon-Gaussian cases. The system performance can be further improved in this framework by using more advanced multiple model methods.A number of simulation results demonstrated that the developed methods have been successfully applied to analyze and design active vehicle suspension systems and can be applied to other applications, such as other mechatronic systems and active building control systems.

As online casino and betting increases in popularity across the globe, the importance of green gambling has become an important subject of discussion. The Swedish betting company, ATG, realises the benefits of this and would like to prevent their gamblers from falling into problem gambling. To predict problem gambling, Bayesian networks were trained on previously identified problem gamblers, separated into seven risk groups. The network was then able to predict the risk group of previously unseen gamblers with an ac- curacy of 94%. It also achieved an average precision of 89%, an average recall of 96% and an average f1-score of 93%. The features in the data set were also ranked, to find which were most important in predicting problem gambling. It was found that municipality, which day of the week the transaction was made and during which hour of the day were the most important features. Also, the Bayesian network was also made as simple as possible, by removing irrelevant features and features which carry very low importance.

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