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Dissertations / Theses on the topic 'Bayesian intelligence'
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1
Horsch, Michael C. "Dynamic Bayesian networks." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.
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Given the complexity of the domains for which we would like to use computers as reasoning
engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications,
the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a class of mathematical structures called Bayesian networks.
This thesis presents a framework for dynamically constructing and evaluating Bayesian networks. In particular, this thesis investigates the issue of representing probabilistic knowledge which has been abstracted from particular individuals to which this knowledge
may apply, resulting in a simple representation language. This language makes the independence assumptions for a domain explicit.
A simple procedure is provided for building networks from knowledge expressed in this language. The mapping between the knowledge base and network created is precisely defined, so that the network always represents a consistent probability distribution.
Finally, this thesis investigates the issue of modifying the network after some evaluation has taken place, and several techniques for correcting the state of the resulting model are derived.<br>Science, Faculty of<br>Computer Science, Department of<br>Graduate
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Edgington, Padraic D. "Modular Bayesian filters." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3712276.
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<p> In 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.</p>
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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/.
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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles with many particles with high inter-particle correlation. Frequent resampling, however, leads to a lack of diversity amongst the particle set in a problem known as sample impoverishment. Traditional sequential Monte Carlo methods attempt to resolve this correlated problem however introduce further data processing issues leading to minimal to comparable performance improvements over the sequential Monte Carlo particle filter. A new method, the adaptive path particle filter, is proposed for recursive Bayesian estimation of non-linear non-Gaussian dynamical systems. Our method addresses the weight degeneracy and sample impoverishment problem by embedding a computational intelligence step of adaptive path switching between generations based on maximal likelihood as a fitness function. Preliminary tests on a scalar estimation problem with non-linear non-Gaussian dynamics and a non-stationary observation model and the traditional univariate stochastic volatility problem are presented. Building on these preliminary results, we evaluate our adaptive path particle filter on the stochastic volatility estimation problem. We calibrate the Heston stochastic volatility model employing a Markov chain Monte Carlo on six securities. Finally, we investigate the efficacy of sequential Monte Carlos for recursive Bayesian estimation of astrophysical time series. We posit latent dynamics for both regularized and irregular astrophysical time series, calibrating fifty-five quasar time series using the CAR(1) model. We find the adaptive path particle filter to statistically significantly outperform the standard sequential importance resampling particle filter, the Markov chain Monte Carlo particle filter and, upon Heston model estimation, the particle learning algorithm particle filter. In addition, from our quasar MCMC calibration we find the characteristic timescale τ to be first-order stable in contradiction to the literature though indicative of a unified underlying structure. We offer detailed analysis throughout, and conclude with a discussion and suggestions for future work.
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Ross, 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.
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Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally from experience in unknown systems. A major problem for such learning algorithms is how to balance optimally the exploration of the system, to gather knowledge, and the exploitation of current knowledge, to complete the task. Model-based Bayesian Reinforcement Learning (BRL) methods provide an optimal solution to this problem by formulating it as a planning problem under uncertainty. However, the complexity of these methods has so far limited their applicability to small and simple domains. To improve the applicability of model-based BRL, this thesis presents several extensions to more complex and realistic systems, such as partially observable and continuous domains. To improve learning efficiency in large systems, this thesis includes another extension to automatically learn and exploit the structure of the system. Approximate algorithms are proposed to efficiently solve the resulting inference and planning problems.<br>L'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.
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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.
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Thesis (M.S. in National Security Affairs) Naval Postgraduate School, September 1992.<br>Thesis advisor: Edward J. Laurance. ADA257717. "September 1992". Includes bibliographical reference (p. 82-84).
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Luo, Zhiyuan. "A probabilistic reasoning and learning system based on Bayesian belief networks." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1490.
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Pomerantz, 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.
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In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the same time, our model has enough degrees of freedom to handle users with different preferences in different contexts. We show on a real data set that using a Bayesian Network to model contexts reduces the error on cross-validation over models that do not link contexts together or ignore context altogether.<br>Dans 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.
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Carr, S. "Investigating the applicability of bayesian networks to the analysis of military intelligence." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2826.
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Intelligence failures have been attributed to an inability to correlate many small pieces
of data into a larger picture. This thesis has sought to investigate how the fusion and
analysis of uncertain or incomplete data through the use of Bayesian Belief Networks
(BBN) compares with people’s intuitive judgements. These flexible, robust, graphical
probabilistic networks are able to incorporate values from a wide range of sources
including empirical values, experimental data and subjective values. Using the latter,
elicited from a number of serving military officers, BBNs provide a logical
framework to combine each individual’s set of one-at-a-time judgements, allowing
comparisons with the same individuals’ many-at-a-time, direct intuitive judgements.
This was achieved through a serie s of fictitious and historical case studies.
Building upon this work, another area of interest was the extent to which different
elicitation techniques lead to equivalent or differing judgements. The techniques
compared were: direct ranking of the variables’ perceived importance for
discriminating between given hypotheses, likelihood ratios and conditional
probabilities. The experimental results showed that individuals were unable to
correctly manipulate the dependencies between information as evidence accumulated.
The results also showed varying beliefs about the importance of information
depending upon the elicitation technique used. Little evidence was found of a high
correlation between direct normative rankings of variables’ importance and those
obtained from the BBNs’ combination of one-at-a-time judgements. Likelihood
values should only be used as an elicitation technique by those who either regularly
manipulate uncertain information or use ratios. Overall, conditional probability
distributions provided the least troublesome elicitation technique of subjective
preferences.
In conclusion, Bayesian Belief Networks developed through the use of subjective
probability distributions offer a flexible, robust methodology for the development of a
normative model for the basis of a decision support system for the quantitative
analysis of intelligence data.
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Jaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.
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Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. This paper discusses the theory and applications of Bayesian networks.
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Saini, 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.
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Background: Modern Code Review (MCR) is a process of reviewing code which is a commonly used practice in software development. It is the process of reviewing any new code changes that need to be merged with the existing codebase. As a developer, one receives many code review requests daily that need to be reviewed. When the developer receives the review requests, they are not prioritised. Manuallyprioritising them is a challenging and time-consuming process. Objectives: This thesis aims to address and solve the above issues by developing a machine intelligence-based code review prioritisation tool. The goal is to identify the factors that impact code review prioritisation process with the help of feedback provided by experienced developers and literature; these factors can be used to develop and implement a solution that helps in prioritising the code review requests automatically. The solution developed is later deployed and evaluated through user and reviewer feedback in a real large-scale project. The developed prioritisation tool is named as Pineapple. Methods: A case study has been conducted at Ericsson. The identification of factors that impact the code review prioritisation process was identified through literature review and semi-structured interviews. The feasibility, usability, and usefulness of Pineapple have been evaluated using a static validation method with the help of responses provided by the developers after using the tool. Results: The results indicate that Pineapple can help developers prioritise their code review requests and assist them while performing code reviews. It was found that the majority of people believed Pineapple has the ability to decrease the lead time of the code review process while providing reliable prioritisations. The prioritisations are performed in a production environment with an average time of two seconds. Conclusions: The implementation and validation of Pineapple suggest the possible usefulness of the tool to help developers prioritise their code review requests. The tool helps to decrease the code review lead-time, along with reducing the workload on a developer while reviewing code changes.
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Gurney, 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/.
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The purpose of this study was to determine whether naïve Bayesian artificial intelligence could accurately predict clinical decisions made during the referral refinement of Chronic open angle glaucoma (COAG) by three specialist independent prescribing optometrists using the highly structured standard operating procedure (SOP) adopted by the Community Ophthalmology Team (COT) of the West Kent Clinical Commissioning Group (CCG). The effectiveness of the COT, in terms of reducing false positive referrals and costs to the National Health Service (NHS), was also explored. This was the first study of its kind. Treating the study as a clinical audit allowed collection of unconsented fully anonymised data from the worst affected eyes or right eyes of 1006 cases referred into the COT. Each case was classified according to race, sex, age, family history of COAG, reason for referral, intraocular pressure and its inter-ocular asymmetry (Goldmann Applanation Tonometry), several optic nerve head dimensions (vertical size, cup disc ratio and its inter-ocular asymmetry; dilated stereoscopic slit lamp biomicroscopy with Volk lens), central corneal thickness (ultrasound pachymetry) and the severity of any visual field defects (Humphrey Visual Field Analyser, SITA FAST 24-2 testing strategy, Hodapp-Parrish-Anderson classification). Grouping of data into multiple cut-off points was informed by previous research and National Institute for Health and Care Excellence (NICE) guidelines. Preliminary analyses showed that most cases (79%) were discharged, 7% were followed up and 14% were referred to the NHS hospital eye service. The high discharge rate led to NHS cost savings of over £50 per case. Previous reports of increased intraocular pressure with central corneal thickness and increased cup disc ratios with cup disc size were also confirmed. Despite a high degree of inter-dependency between clinical tests, which violated the key assumption of naïve Bayesian analyses, the scheme learned rapidly and its weighted accuracy, based on randomised stratified tenfold cross-validation, was high (95%, 2.0% SD). However, false discharge (3.4%, 1.6% SD) and referral rates (3.1%, 1.5% SD) were considered unsafe. Making the analysis cost sensitive led to an 80 fold increase in COT follow-ups that would have reduced cost effectivity. The transferability of likelihood ratios was explored along with their use, compared to Chi-square, to rank clinical tests and explore redundancy in the SOP adopted by the COT. In summary, high discharge rates were consistent with the level of false positive referrals for suspected COAG reported in the literature and reduced NHS costs. Although use of a structured SOP led to high accuracy, naïve Bayesian artificial intelligence could not safely predict the decisions of COT optometrists as it caused too many false discharges and referrals. More sophisticated forms of machine learning need to be explored.
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Suermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.
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Roussos, 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.
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In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or 'sources' via a generally unknown mapping. Reconstructing sources from their mixtures is an extremely ill-posed problem in general. However, solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian method- ology, allowing us to incorporate "soft" constraints in a natural manner. This Thesis proposes the use of sparse statistical decomposition methods for ex- ploratory analysis of datasets. We make use of the fact that many natural signals have a sparse representation in appropriate signal dictionaries. The work described in this Thesis is mainly driven by problems in the analysis of large datasets, such as those from functional magnetic resonance imaging of the brain for the neuro-scientific goal of extracting relevant 'maps' from the data. We first propose Bayesian Iterative Thresholding, a general method for solv- ing blind linear inverse problems under sparsity constraints, and we apply it to the problem of blind source separation. The algorithm is derived by maximiz- ing a variational lower-bound on the likelihood. The algorithm generalizes the recently proposed method of Iterative Thresholding. The probabilistic view en- ables us to automatically estimate various hyperparameters, such as those that control the shape of the prior and the threshold, in a principled manner. We then derive an efficient fully Bayesian sparse matrix factorization model for exploratory analysis and modelling of spatio-temporal data such as fMRI. We view sparse representation as a problem in Bayesian inference, following a ma- chine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. The performance and utility of the proposed algorithms is demonstrated on a variety of experiments using both simulated and real datasets. Experimental results with benchmark datasets show that the proposed algorithms outper- form state-of-the-art tools for model-free decompositions such as independent component analysis.
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Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.
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This dissertation explores a new challenge to digital systems posed by the adaptation of mobile
devices and proposes a countermeasure to secure systems against threats to this new digital
ecosystem.
The study provides the reader with background on the topics of spam, Botnets and machine
learning before tackling the issue of mobile spam.
The study presents the reader with a three tier model that uses machine learning techniques to
combat spamming mobile Botnets. The three tier model is then developed into a prototype and
demonstrated to the reader using test scenarios.
Finally, this dissertation critically discusses the advantages of having using the three tier model
to combat spamming Botnets.<br>Dissertation (MSc)--University of Pretoria, 2013.<br>gm2014<br>Computer Science<br>unrestricted
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Ceccon, Stefano. "Extending Bayesian network models for mining and classification of glaucoma." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8051.
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Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.
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Groves, 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.
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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and therefore may have common noise sources, and a method for automatically modelling this correlation is developed. A Gaussian process prior is also used to allow spatial regularization while simultaneously applying informative priors on model parameters, restricting biophysically-interpretable parameters to reasonable values. The second part introduces a novel data fusion framework for multivariate data analysis which finds a joint decomposition of data across several modalities using a shared loading matrix. Each modality has its own generative model, including separate spatial maps, noise models and sparsity priors. This flexible approach can perform supervised learning by using target variables as a modality. By inferring the data decomposition and multivariate decoding simultaneously, the decoding targets indirectly influence the component shapes and help to preserve useful components. The same framework is used for unsupervised learning by placing independent component analysis (ICA) priors on the spatial maps. Linked ICA is a novel approach developed to jointly decompose multimodal data, and is applied to combined structural and diffusion images across groups of subjects. This allows some of the benefits of tensor ICA and spatially-concatenated ICA to be combined, and allows model comparison between different configurations. This joint decomposition framework is particularly flexible because of its separate generative models for each modality and could potentially improve modelling of functional MRI, magnetoencephalography, and other functional neuroimaging modalities.
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Rey, 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/.
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O objetivo da dissertação é explorar Redes Bayesianas como ferramenta para reduzir incertezas nos processos de Inteligência Competitiva. Nela, através da revisão de conceitos de Planejamento Estratégico, Tomada de Decisão, Inteligência Competitiva e da capacidade de inferência de Redes Bayesianas é proposta uma abordagem de utilização destas redes com este intuito. Para tanto um estudo de caso apresenta o passo a passo da implementação da abordagem proposta em um ambiente simulado de gestão. No estudo de caso, cada uma das etapas da modelagem de cenários é descrita em detalhes, salientando os cuidados necessários para esta modelagem. Com a modelagem finalizada, dois quase-experimentos foram conduzidos em ambientes simulados para avaliar a percepção e o desempenho dos tomadores de decisão que utilizaram Redes Bayesianas em relação aos tomadores de decisão que não a utilizaram. Os dados obtidos no primeiro quase-experimento não se mostraram confiáveis e no segundo quase-experimento não formaram uma amostra significativa do ponto de vista estatístico. Não obstante, foi possível apresentar contribuições através das observações e dados obtidos nestes quaseexperimentos conduzidos. Do ponto de vista processual, falhas na construção dos quaseexperimento e sugestões de melhoria foram apresentadas. Quanto à ferramenta modelada e construída com base em Redes Bayesianas, foi possível identificar percepções do usuário relativas ao seu uso e sugestões de como aprimorá-la. Quanto aos dados de desempenho obtido, foi possível analisar, no segundo quase-experimento, indícios, mesmo que não conclusivos, que justificam a proposição de novos estudos para aprofundamento. Com base na literatura e nos indícios obtidos é possível acreditar que Redes Bayesianas podem ser usadas na redução de incerteza nos processos de inteligência competitiva e de tomada de decisão.<br>The 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.
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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.
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Gopalakrishnan, Arjun. "Probabilistic Analysis of Contracting Ebola Virus Using Contextual Intelligence." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984182/.
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The outbreak of the Ebola virus was declared a Public Health Emergency of International Concern by the World Health Organisation (WHO). Due to the complex nature of the outbreak, the Centers for Disease Control and Prevention (CDC) had created interim guidance for monitoring people potentially exposed to Ebola and for evaluating their intended travel and restricting the movements of carriers when needed. Tools to evaluate the risk of individuals and groups of individuals contracting the disease could mitigate the growing anxiety and fear. The goal is to understand and analyze the nature of risk an individual would face when he/she comes in contact with a carrier. This thesis presents a tool that makes use of contextual data intelligence to predict the risk factor of individuals who come in contact with the carrier.
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Abu-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.
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Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning algorithms on large networks. The selected algorithms then serve as a committee, which then votes on the final network structure. The result is a more selective final network, containing few false positives, with compromised ability to detect all network features.
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Dearden, 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.
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Pieropan, 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/.
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In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries.
Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task.
The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect.
This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors.
The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning.
Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable.
Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.
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Aguiar, 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.
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As teorias que sustentam os modelos de precificação têm obtido resultados pouco satisfatórios ou insatisfatórios, uma vez que em cada estudo busca aproximar-se da realidade por apenas uma face, não observando o problema de todos os ângulos. Nesse sentido, percebeu-se um gap nos estudos de previsão, explorar sob outras lentes a dinâmica das variáveis do mercado que influenciam a formação do preço para o seu prévio monitoramento. Assim, o objetivo desta pesquisa foi construir uma ferramenta de apoio à decisão que pudesse prever, periodicamente, o preço futuro de uma commodity a curto e médio prazo, notadamente para o butadieno, um derivado do petróleo. Para que isto fosse possível, foi realizada a datação dos pontos de mudança do preço dessa commodity, frente aos acontecimentos históricos e, a partir daí, construído o estudo sobre três estruturas: mercado, política e econômica. A partir de então, observou-se quais seriam as variáveis mais consistentes para formar a base da pesquisa. As previsões obtidas revelam um desempenho superior às pesquisas anteriormente realizadas. Assim, a análise da previsão dos pontos de mudança constitui um instrumento informativo para sinalizar o comportamento futuro do preço da commodity do butadieno. A ferramenta utilizada para o modelo de precificação de modo a compreender a natureza das flutuações foram as Redes Bayesianas, que apresentam a capacidade de expressar as probabilidades e de um conjunto de variáveis aleatórias previamente definidas, e fazer predições adequadas. A inferência sobre o preço da commodity do butadieno, a curto e médio prazo, é realizada com o auxílio do software GeNIe 2.0. Conclui-se que investir em pesquisas que utilizem de Inteligência Artificial como métodos preditivos, como a utilização de Redes Bayesianas apresenta a vantagem de compreender a relação causa e efeito através da análise de Cenários. Assim, o objetivo de construir uma ferramenta de apoio à decisão que pudesse prever, periodicamente, o preço do butadieno a curto e médio prazo, foi alcançado. Para determinado período houve 84% de chances de acerto nas previsões.<br>The 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.
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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.
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In this dissertation, a normative prescriptive paradigm, namely a Bayesian theory of content-dependent argument strength, was employed in order to investigate argumentation, specifically the classic fallacy of the &ldquo<br>argument from ignorance&rdquo<br>or &ldquo<br>argumentum ad ignorantiam&rdquo<br>. 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<br>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<br>DI or &ndash<br>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.
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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.
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Orientador: Jacques Wainer<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-09T21:11:41Z (GMT). No. of bitstreams: 1
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Previous issue date: 2007<br>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<br>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<br>Doutorado<br>Sistemas de Informação<br>Doutor em Ciência da Computação
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Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.
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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>William Henry Hsu<br>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.
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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/.
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A segurança no tráfego de carros é um assunto em foco nos dias de hoje e, dentro dele, podem-se citar os sistemas de auxílio ao motorista que vêm sendo desenvolvidos com a finalidade de reduzir o grande número de fatalidades em acidentes de trânsito. Tais sistemas de auxílio buscam mitigar falhas humanas como falta de atenção e imprudência. Visto isso, o projeto SENA, desenvolvido pelo Laboratório de Robótica Móvel da Escola de Engenharia de São Carlos, busca contribuir com a evolução dessa assistência ao motorista. O presente trabalho realiza um estudo sobre uma técnica de inteligência artificial chamada de Redes Bayesianas. Essa técnica merece atenção em virtude de sua capacidade de tratar dados incertos em forma de probabilidades. A rede desenvolvida por esse trabalho utiliza, como dados de entrada, os classificadores em desenvolvimento no projeto SENA e tem como resposta um comportamento que o veículo deve executar, por um ser humano ou por um planejador de trajetórias. Em função da alta dimensionalidade do problema abordado, foram realizados dois experimentos em ambiente simulado de duas situações distintas. A primeira, um teste de frenagem próximo a um ponto de intersecção e a segunda, um cenário de entroncamento. Os testes feitos com a rede indicam que classificadores pouco discriminantes deixam o sistema mais propenso a erros e que erros na localização do ego-veículo afetam mais o sistema se comparado a erros na localização dos outros veículos. Os experimentos realizados mostram a necessidade de um sistema de tempo real e um hardware mais adequado para tratar as informações mais rapidamente<br>The 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
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Meganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.
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A partir de données d'observation classiques, il est rarement possible d'arriver à une structure de réseau bayésien qui soit complètement causale. Le point théorique auquel nous nous intéressons est l'apprentissage des réseaux bayésiens causaux, avec ou sans variables latentes. Nous nous sommes d'abord focalisés sur la découverte de relations causales lorsque toutes les variables sont connues (i.e. il n'y a pas de variables latentes) en proposant un algorithme d'apprentissage utilisant à la fois des données issues d'observations et d'expérimentations. Logiquement, nous nous sommes ensuite concentrés sur le même problème lorsque toutes les variables ne sont pas connues. Il faut donc découvrir à la fois des relations de causalité entre les variables et la présence éventuelle de variables latentes dans la structure du réseau bayésien. Pour cela, nous tentons d'unifier deux formalismes, les modèles causaux semi-markoviens (SMCM) et les graphes ancestraux maximaux (MAG), utilisés séparément auparavant, l'un pour l'inférence causale (SMCM), l'autre pour la découverte de causalité (MAG). Nous nous sommes aussi interessé à l'adaptation de réseaux bayésiens causaux pour des systèmes multi-agents, et sur l'apprentissage de ces modèles causaux multi-agents (MACM).
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Suvorov, 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.
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My dissertation comprises three related topics of evolutionary and computational biology, which correspond to the three Chapters. Chapter 1 focuses on tempo and mode of evolution in visual genes, namely opsins, via duplication events and subsequent molecular adaptation in Odonata (dragonflies and damselflies). Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Odonata have the largest opsin repertoire of any insect currently known. In particular our results suggest that both the blue sensitive (BS) and long-wave sensitive (LWS) opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level. Chapter 2 primarily focuses on reconstruction of the phylogenetic backbone of Odonata using RNA-seq data. In order to reconstruct the evolutionary history of Odonata, we performed comprehensive phylotranscriptomic analyses of 83 species covering 75% of all extant odonate families. Using maximum likelihood, Bayesian, coalescent-based and alignment free tree inference frameworks we were able to test, refine and resolve previously controversial relationships within the order. In particular, we confirmed the monophyly of Zygoptera, recovered Gomphidae and Petaluridae as sister groups with high confidence and identified Calopterygoidea as monophyletic. Fossil calibration coupled with diversification analyses provided insight into key events that influenced the evolution of Odonata. Specifically, we determined that there was a possible mass extinction of ancient odonate diversity during the P-Tr crisis and a single odonate lineage persisted following this extinction event. Lastly, Chapter 3 focuses on identification of erroneously assigned sequence homology using the intelligent agents of machine learning techniques. Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. We developed biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes.
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Howell, 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.
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Thesis (Ph.D.)--RAND Graduate School, 2009.<br>"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.
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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.
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Orientador: Fernando José Von Zuben<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-26T06:49:07Z (GMT). No. of bitstreams: 1
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Previous issue date: 2012<br>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<br>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<br>Doutorado<br>Engenharia de Computação<br>Doutor em Engenharia Elétrica
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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/.
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Este estudo visa compreender a adequação dos rendimentos às necessidades e condições de vida dos brasileiros. Observando os dados da Pesquisa de Orçamentos Familiares (POF) realizada pelo IBGE (Instituto Brasileiro de Geografia e Estatística) para o período: 2008 e 2009, o estudo identifica um modelo que se concentra na investigação sobre o fato de 75% dos domicílios brasileiros declararem dificuldades orçamentárias. Para desenvolver um modelo, foi utilizada a percepção declarada e subjetiva de adequação da renda, informada pelo chefe de família ou pessoa de referência no domicílio. O referencial teórico baseia-se no comportamento do consumidor e foca nos recursos econômicos. O método quantitativo foi desenvolvido com Inteligência Artificial, mais especificamente Redes Bayesianas. Redes Bayesianas são estruturas em forma de grafos onde as distribuições de probabilidade são representadas por nós ligados por arcos acíclicos, que podem representar ou não relações causais entre as variáveis. No final pretende-se contribuir para o conhecimento e melhoria no desenho de políticas públicas e para as empresas em geral, dando um panorama sobre o que afeta as dificuldades das famílias, proporcionando uma visão que vai além da tradicional divisão de classes econômicas.<br>This 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.
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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.
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Coursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.
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Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.
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Osunmakinde, Isaac Olusegun. "Computational intelligent systems : evolving dynamic Bayesian networks." Doctoral thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/6429.
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Includes abstract.<br>Includes bibliographical references (p. 163-172).<br>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.
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Ben, Mrad Ali. "Observations probabilistes dans les réseaux bayésiens." Thesis, Valenciennes, 2015. http://www.theses.fr/2015VALE0018/document.
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Dans un réseau bayésien, une observation sur une variable signifie en général que cette variable est instanciée. Ceci signifie que l’observateur peut affirmer avec certitude que la variable est dans l’état signalé. Cette thèse porte sur d’autres types d’observations, souvent appelées observations incertaines, qui ne peuvent pas être représentées par la simple affectation de la variable. Cette thèse clarifie et étudie les différents concepts d’observations incertaines et propose différentes applications des observations incertaines dans les réseaux bayésiens.Nous commençons par dresser un état des lieux sur les observations incertaines dans les réseaux bayésiens dans la littérature et dans les logiciels, en termes de terminologie, de définition, de spécification et de propagation. Il en ressort que le vocabulaire n'est pas clairement établi et que les définitions proposées couvrent parfois des notions différentes.Nous identifions trois types d’observations incertaines dans les réseaux bayésiens et nous proposons la terminologie suivante : observation de vraisemblance, observation probabiliste fixe et observation probabiliste non-fixe. Nous exposons ensuite la façon dont ces observations peuvent être traitées et propagées.Enfin, nous donnons plusieurs exemples d’utilisation des observations probabilistes fixes dans les réseaux bayésiens. Le premier exemple concerne la propagation d'observations sur une sous-population, appliquée aux systèmes d'information géographique. Le second exemple concerne une organisation de plusieurs agents équipés d'un réseau bayésien local et qui doivent collaborer pour résoudre un problème. Le troisième exemple concerne la prise en compte d'observations sur des variables continues dans un RB discret. Pour cela, l'algorithme BN-IPFP-1 a été implémenté et utilisé sur des données médicales de l'hôpital Bourguiba de Sfax<br>In 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
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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.
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Par rapport aux exigences de plus en plus importantes relatives au Maintien en Condition Opérationnelle d'un système industriel, le processus de maintenance joue un rôle fondamental pour l'amélioration de la disponibilité, de la productivité, etc. Pour essayer de contrôler au mieux ces performances, les responsables de maintenance doivent donc être capables de choisir les stratégies de maintenance et les ressources à mettre en oeuvre les plus adaptées aux besoins. Dans un objectif d'aide à la prise de décisions en maintenance, les travaux présentés dans ce mémoire ont pour objet de proposer une méthodologie pour l'élaboration d'un modèle support permettant par simulation d'évaluer les différentes stratégies. La valeur ajoutée de la méthodologie réside dans l'unification, à base de modèles relationnels probabilistes (PRM), des différents types de connaissance nécessaires à la construction de ce modèle d'évaluation. Ce dernier est ainsi construit à partir de motifs génériques et modulables représentatifs des variables décisionnels du système industriel (système principal) et de son système de maintenance. Ces motifs, par instanciation, facilitent la construction des modèles d'applications spécifiques. Cette méthodologie, issue du projet ANR SKOOB, est testée sur le cas applicatif de la maintenance d'un système de production de ferment<br>Nowadays, 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
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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.
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Ce travail de recherche est consacré à la formalisation et à la réalisation d'un processus de diagnostic pluridisplinaire. La particularité d'un tel diagnostic résulte du fait qu'il nécessite de nombreux spécialistes, chacun ayant des connaissances sur leur domaine. Le problème principal réside dans les interconnexions entre les domaines. Ces interconnexions peuvent ou non être connues et influer sur le diagnostic. Dans ce manuscrit, nous proposons de réaliser un diagnostic pluridisciplinaire l'aide d'un système multi-agents. Les agents élaborent un diagnostic local à un domaine puis, fusionnent leurs diagnostics afin d'obtenir le diagnostic pluridisciplinaire. Dans ce but, nous proposons un cadre d'argumentation et une méthode de fusion des diagnostics. Ensemble, ces deux propositions forment le modèle ANDi.
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Ben, 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.
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En réponse au besoin croissant de réutiliser les connaissances déjà existantes lors de l'apprentissage des réseaux bayésiens causaux, les connaissances sémantiques contenues dans les ontologies de domaine présentent une excellente alternative pour assister le processus de découverte causale avec le minimum de coût et d'eff ort. Dans ce contexte, la présente thèse s'intéresse plus particulièrement au crossing-over entre les réseaux bayésiens causaux et les ontologies et établit les bases théoriques d'une approche cyclique intégrant les deux formalismes de manière interchangeable. En premier lieu, on va intégrer les connaissances sémantiques contenues dans les ontologies de domaine pour anticiper les meilleures expérimentations au travers d'une stratégie fortuite (qui, comme son nom l'indique, mise sur l'imprévu pour dégager les résultats les plus impressionnants). En e et, les connaissances sémantiques peuvent inclure des relations causales en plus de la structure hiérarchique. Donc au lieu de refaire les mêmes efforts qui ont déjà été menés par les concepteurs et éditeurs d'ontologies, nous proposons de réutiliser les relations (sémantiquement) causales en les adoptant comme étant des connaissances à priori. Ces relations seront alors intégrées dans le processus d'apprentissage de structure (partiellement) causale à partir des données d'observation. Pour compléter l'orientation du graphe causal, nous serons en mesure d'intervenir activement sur le système étudié. Nous présentons également une stratégie décisionnelle basée sur le calcul de distances sémantiques pour guider le processus de découverte causale et s'engager davantage sur des pistes inexplorées. L'idée provient principalement du fait que les concepts les plus rapprochés sont souvent les plus étudiés. Pour cela, nous proposons de renforcer la capacité des ordinateurs à fournir des éclairs de perspicacité en favorisant les expérimentations au niveau des concepts les plus distants selon la structure hiérarchique. La seconde direction complémentaire concerne un procédé d'enrichissement par lequel il sera possible de réutiliser ces découvertes causales et soutenir le caractère évolutif de l'ontologie. Une étude expérimentale a été conduite en utilisant les données génomiques concernant Saccharomyces cerevisiae et l'Ontologie des Gènes pour montrer les potentialités de l'approche SemCaDo dans des domaines ou les expérimentations sont généralement très coûteuses, complexes et fastidieuses.
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Coles, 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.
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This thesis is concerned with the design and analysis of new Bayesian network based mobility algorithms for mobile Wireless Sensor Networks (WSNs). The hypothesis for the work presented herein is that incorporating Artificial Intelligence (Al) at the level of the sensor nodes will improve their performance (coverage, connectivity and lifetime) and result in fault tolerance capabilities, in the face of uncertainty associated with incomplete information regarding the network. Two types of mobility strategy are presented and investigated. Firstly, a new gazing mobility strategy is presented which is biologically inspired from herbivores grazing pastures. As part of the latter strategy, and instead of deploying a large number of static sensor nodes to cover a region of interest, a smaller number of mobile nodes are deployed which migrate around the region to achieve coverage over time. To enable the performance evaluation of this strategy a new coverage measure called Coverage Against Time was created. A new decentralised Bayesian network based grazing mobility algorithm called BNGRAZ is presented which uses evidence derived from neighbouring nodes to predict the probability of performance (coverage and connectivity) changes associated with moving in a particular direction. Evidence is also obtained from a new Coverage Approximation (CA) algorithm which enables each sensor node to approximate the WSN coverage in order to determine areas in need of servicing. The performance of BNGRAZ is compared to a fixed path mobility technique, Random Waypoint (RWP) mobility model, and a new Grazing Reference Point Group Mobility (GRPGM) algorithm developed as part of this work. Secondly, a self-healing strategy which physically relocates sensor nodes to repair coverage holes, due to the failure of sensor nodes, is presented. A new decentralised Bayesian network based mobility algorithm called BayesMob, which uses local neighbour information, was created to coordinate the self-healing motion. The algorithm enables sensor nodes to predict the probability of an increase in coverage given a move in a particular direction to repair coverage holes. In addition, the thesis outlines the development of a WSN simulator. The latter provides a tool for evaluating the performance of mobile WSNs. All mobility strategies and algorithms discussed herein were simulated using the new WSN simulator.
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Gravem, 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.
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In this thesis, we present an approach to integration of case-based reasoning and Bayesian reasoning for decision support. Our design is meant to provide physicians with decision support in the context of palliative care for lung cancer patients. Because of delays in the medical data, we created an intermediate application with the aim to assist people in choosing an adequate wine for a given meal. We have developed a system that is able to utilize both the general knowledge of the Bayesian network and the specialized knowledge of the case base. Our results shows that the combination of CBR and BN are able to discover solutions that would not been found by using only one of the methodologies.
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Campbell, 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.
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Schlenoff, Craig. "Inferring intentions through state representations in cooperative human-robot environments." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS064/document.
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Les humains et les robots travaillant en toute sécurité et en parfaite harmonie dans un environnement est l'un des objectifs futurs de la communauté robotique. Quand les humains et les robots peuvent travailler ensemble dans le même espace, toute une catégorie de tâches devient prête à l'automatisation, allant de la collaboration pour l'assemblage de pièces, à la manutention de pièces et de materiels ainsi qu'à leur livraison. Garantir la sûreté des humains nécessite que le robot puisse être capable de surveiller la zone de travail, déduire l'intention humaine, et être conscient suffisamment tôt des dangers potentiels afin de les éviter.Des normes existent sur la collaboration entre robots et humains, cependant elles se focalisent à limiter les distances d'approche et les forces de contact entre l'humain et le robot. Ces approches s'appuient sur des processus qui se basent uniquement sur la lecture des capteurs, et ne tiennent pas compte des états futurs ou des informations sur les tâches en question. Un outil clé pour la sécurité entre des robots et des humains travaillant dans un environnement inclut la reconnaissance de l'intention dans lequel le robot tente de comprendre l'intention d'un agent (l'humain) en reconnaissant tout ou partie des actions de l'agent pour l'aider à prévoir les actions futures de cet agent. La connaissance de ces actions futures permettra au robot de planifier sa contribution aux tâches que l'humain doit exécuter ou au minimum, à ne pas se mettre dans une position dangereuse.Dans cette thèse, nous présentons une approche qui est capable de déduire l'intention d'un agent grâce à la reconnaissance et à la représentation des informations de l'état. Cette approche est différente des nombreuses approches présentes dans la littérature qui se concentrent principalement sur la reconnaissance de l'activité (par opposition à la reconnaissance de l'état) et qui « devinent » des raisons pour expliquer les observations. Nous déduisons les relations détaillées de l'état à partir d'observations en utilisant Region Connection Calculus 8 (RCC-8) et ensuite nous déduisons les relations globales de l'état qui sont vraies à un moment donné. L'utilisation des informations sur l'état sert à apporter une contribution plus précise aux algorithmes de reconnaissance de l'intention et à générer des résultats qui sont equivalents, et dans certains cas, meilleurs qu'un être humain qui a accès aux mêmes informations<br>Humans 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
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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.
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Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in developing firefighting robots that autonomously locate a fire inside of a structure that is not in the direct robot field of view. The commonly used sensors for robots cannot properly function in fire smoke-filled environments where high temperature and zero visibility are present. Also, the existing obstacle avoidance methods have limitations calculating safe trajectories and solving local minimum problem while avoiding obstacles in real time under cluttered and dynamic environments. In addition, research for characterizing fire environments to provide firefighting robots with proper headings that lead the robots to ultimately find the fire is incomplete.
For use on intelligent firefighting robots, this research developed a real-time local obstacle avoidance method, local dynamic goal-based fire location, appropriate feature selection for fire environment assessment, and probabilistic classification of fire, smoke and their thermal reflections. The real-time local obstacle avoidance method called the weighted vector method is developed to perceive the local environment through vectors, identify suitable obstacle avoidance modes by applying a decision tree, use weighting functions to select necessary vectors and geometrically compute a safe heading. This method also solves local obstacle avoidance problems by integrating global and local goals to reach the final goal. To locate a fire outside of the robot field of view, a local dynamic goal-based 'Seek-and-Find' fire algorithm was developed by fusing long wave infrared camera images, ultraviolet radiation sensor and Lidar. The weighted vector method was applied to avoid complex static and unexpected dynamic obstacles while moving toward the fire. This algorithm was successfully validated for a firefighting robot to autonomously navigate to find a fire outside the field of view.
An improved 'Seek-and-Find' fire algorithm was developed using Bayesian classifiers to identify fire features using thermal images. This algorithm was able to discriminate fire and smoke from thermal reflections and other hot objects, allowing the prediction of a more robust heading for the robot. To develop this algorithm, appropriate motion and texture features that can accurately identify fire and smoke from their reflections were analyzed and selected by using multi-objective genetic algorithm optimization. As a result, mean and variance of intensity, entropy and inverse difference moment in the first and second order statistical texture features were determined to probabilistically classify fire, smoke, their thermal reflections and other hot objects simultaneously. This classification performance was measured to be 93.2% accuracy based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5 - 99.9% for classifying fire and smoke using the test dataset.<br>Ph. D.
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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.
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Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported.
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Qiu, Yumeng. "Leveraging Influential Factors into Bayesian Knowledge Tracing." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/53.
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Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience in the domain, leading to models without performance improvement. Moreover, KT is computationally expensive, model fitting procedures can take hours or days to run on large datasets. The goal of this research work is to improve the accuracy of student performance prediction by incorporating the memory decay factor which the standard Bayesian Knowledge Tracing had ignored. We also propose a completely data driven and inexpensive approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvements based purely on the dataset features that are computed from ITS system logs.
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Sahin, Ferat. "A Bayesian Network Approach to the Self-organization and Learning in Intelligent Agents." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29034.
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A Bayesian network approach to self-organization and learning is introduced for use with intelligent agents. Bayesian networks, with the help of influence diagrams, are employed to create a decision-theoretic intelligent agent. Influence diagrams combine both Bayesian networks and utility theory. In this research, an intelligent agent is modeled by its belief, preference, and capabilities attributes. Each agent is assumed to have its own belief about its environment. The belief aspect of the intelligent agent is accomplished by a Bayesian network. The goal of an intelligent agent is said to be the preference of the agent and is represented with a utility function in the decision theoretic intelligent agent. Capabilities are represented with a set of possible actions of the decision-theoretic intelligent agent. Influence diagrams have utility nodes and decision nodes to handle the preference and capabilities of the decision-theoretic intelligent agent, respectively.
Learning is accomplished by Bayesian networks in the decision-theoretic intelligent agent. Bayesian network learning methods are discussed intensively in this paper. Because intelligent agents will explore and learn the environment, the learning algorithm should be implemented online. None of the existent Bayesian network learning algorithms has online learning. Thus, an online Bayesian network learning method is proposed to allow the intelligent agent learn during its exploration.
Self-organization of the intelligent agents is accomplished because each agent models other agents by observing their behavior. Agents have belief, not only about environment, but also about other agents. Therefore, an agent takes its decisions according to the model of the environment and the model of the other agents. Even though each agent acts independently, they take the other agents behaviors into account to make a decision. This permits the agents to organize themselves for a common task.
To test the proposed intelligent agent's learning and self-organizing abilities, Windows application software is written to simulate multi-agent systems. The software, IntelliAgent, lets the user design decision-theoretic intelligent agents both manually and automatically. The software can also be used for knowledge discovery by employing Bayesian network learning a database.
Additionally, we have explored a well-known herding problem to obtain sound results for our intelligent agent design. In the problem, a dog tries to herd a sheep to a certain location, i.e. a pen. The sheep tries to avoid the dog by retreating from the dog. The herding problem is simulated using the IntelliAgent software. Simulations provided good results in terms of the dog's learning ability and its ability to organize its actions according to the sheep's (other agent) behavior.
In summary, a decision-theoretic approach is applied to the self-organization and learning problems in intelligent agents. Software was written to simulate the learning and self-organization abilities of the proposed agent design. A user manual for the software and the simulation results are presented.
This research is supported by the Office of Naval Research with the grant number N00014-98-1-0779. Their financial support is greatly appreciated.<br>Ph. D.
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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.
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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada<br>This 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.
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Lin, Rongbin. "UAV Intelligent Path Planning for Wilderness Search and Rescue." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1759.
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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human-behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes Chi-squared test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors. Once a probability distribution map is in place, areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a Unmanned Aerial Vehicle (UAV) to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this path-planning problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the optimal solution, making efficient use of the limited UAV flying time. The capability of planning a path with a set destination also enables the UAV operator to plan a path strategically while letting the UAV plan the path locally.
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Hawkins, William J. "Boredom and student modeling in intelligent tutoring systems." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/307.
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Over the past couple decades, intelligent tutoring systems (ITSs) have become popular in education. ITSs are effective at helping students learn (VanLehn, 2011; Razzaq, Mendicino & Heffernan, 2008; Koedinger et al, 1997) and help researchers understand how students learn. Such research has included modeling how students learn (Corbett & Anderson, 1995), the effectiveness of help given within an ITS (Beck et al, 2008), the difficulty of different problems (Pardos & Heffernan, 2011), and predicting long-term outcomes like college attendance (San Pedro et al, 2013a), among many other studies.
While most studies have focused on ITSs from a cognitive perspective, a growing number of researchers are paying attention to the motivational and affective aspects of tutoring, which have been recognized as important components of human tutoring (Lepper et al, 1993). Recent work has shown that student affect within an ITS can be detected, even without physical sensors or cameras (D’Mello et al, 2008; Conati & Maclaren, 2009; Sabourin et al, 2011; San Pedro et al, 2013b).
Initial studies with these sensor-less affect detectors have shown that certain problematic affective states, such as boredom, confusion and frustration, are prevalent within ITSs (Baker et al, 2010b). Boredom in particular has been linked to negative learning outcomes (Pekrun et al, 2010; Farmer & Sundberg, 1986) and long-term disengagement (Farrell, 1988). Therefore, reducing or responding effectively to these affective states within ITSs may improve both short- and long-term learning outcomes.
This work is an initial attempt to determine what causes boredom in ITSs. First, we determine which is more responsible for boredom in ITSs: the content in the system, or the students themselves. Based on the findings of that analysis, we conduct a randomized controlled trial to determine the effects of monotony on student boredom. In addition to the work on boredom, we also perform analyses that concern student modeling, specifically how to improve Knowledge Tracing (Corbett & Anderson, 1995), a popular student model used extensively in real systems like the Cognitive Tutors (Koedinger et al, 1997) and in educational research.