O reconhecimento de atividaes é vital no contexto dos ambientes inteligentes. Mesmo com a facilidade de acesso a sensores móveis baratos, reconhecer atividades continua sendo um problema difícil devido à incerteza nas leituras dos sensores e à complexidade das atividades. A teoria da evidência provê um modelo de reconhecimento de atividades que detecta atividades mesmo na presença de incerteza nas leituras dos sensores, mas ainda não é capaz de modelar atividades complexas ou mudanças na configuração dos sensores ou do ambiente. Este trabalho propõe combinar abordagens baseadas em modelagem de conhecimento com a teoria da evidência, melhorando assim a construção dos modelos da última trazendo a reusabilidade, flexibilidade e semântica rica da primeira. / Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.

O reconhecimento de atividaes é vital no contexto dos ambientes inteligentes. Mesmo com a facilidade de acesso a sensores móveis baratos, reconhecer atividades continua sendo um problema difícil devido à incerteza nas leituras dos sensores e à complexidade das atividades. A teoria da evidência provê um modelo de reconhecimento de atividades que detecta atividades mesmo na presença de incerteza nas leituras dos sensores, mas ainda não é capaz de modelar atividades complexas ou mudanças na configuração dos sensores ou do ambiente. Este trabalho propõe combinar abordagens baseadas em modelagem de conhecimento com a teoria da evidência, melhorando assim a construção dos modelos da última trazendo a reusabilidade, flexibilidade e semântica rica da primeira. / Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.

Made available in DSpace on 2014-06-12T15:54:40Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / O presente trabalho estabelece uma extensão à Teoria Matemática da Evidência, também conhecida como Teoria de Dempster-Shafer, através da adoção de uma nova regra de combinação de evidências e de um arcabouço conceitual associado. Essa extensão resolve os problemas de comportamento contra-intuitivo apresentados originalmente pela teoria, amplia o poder expressional da mesma e permite a representação da incerteza nos resultados. A representação da incerteza implica a disponibilidade da mesma como um recurso estratégico a ser utilizado nas decisões baseadas nas evidências combinadas, bem como deixa explícita a relação entre os resultados numéricos obtidos e a probabilidade clássica

Made available in DSpace on 2014-06-12T15:55:07Z (GMT). No. of bitstreams: 2 arquivo9565_1.pdf: 714901 bytes, checksum: e1602dd43ff228b49b6ff591a1e8915a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / O presente trabalho estabelece uma extensão `a Teoria Matemática da Evidência, também conhecida como Teoria de Dempster-Shafer, através da adoção de uma nova regra de combinação de evidências e de um arcabouço conceitual associado. Essa extensão resolve os problemas de comportamento contra-intuitivo apresentados originalmente pela teoria, amplia o poder expressional da mesma e permite a representação da incerteza nos resultados. A representação da incerteza implica a disponibilidade da mesma como um recurso estratégico a ser utilizado nas decisões baseadas nas evidências combinadas, bem como deixa explícita a relação entre os resultados numéricos obtidos e a probabilidade clássica

This research explores the theory and practice of risk assessment and project evaluationand proposes novel alternatives. Reviewing literature revealed a continuous endeavourfor better project risk modelling and analysis. A number of proposals for improving theprevailing Probability-Impact (P-I) risk model can be found in literature. Moreover,researchers have investigated the feasibility of different theories in analysing projectrisk. Furthermore, various decision support systems (DSSs) are available for aidingpractitioners in risk assessment and decision making. Unfortunately, they are sufferingfrom a low take-up. Instead, personal judgment and past experience are mainly used foranalysing risk and making decisions.In this research, a new risk model is proposed through extending the P-I risk model toinclude a third dimension: probability of impact materialisation. Such an extensionreflects the characteristics of a risk, its surrounding environment and the ability ofmitigating its impact. A new assessment methodology is devised. Dempster-ShaferTheory of Evidence (DST) is researched and presented as a novel alternative toProbability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature ofproject risks analysis. A DST-based assessment methodology was developed forstructuring the personal experience and professional judgment of risk analysts andutilising them for risk analysis. Benefiting from the unique features of the EvidentialReasoning (ER) approach, the proposed methodology enables analysts to express theirevaluations in distributed forms, so that they can provide degrees of belief in apredefined set of assessment grades based on available information. This is a veryeffective way for tackling the problem of lack of information which is an inherentfeature of most projects during the tendering stage. It is the first time that such anapproach is ever used for handling construction risk assessment. Monetary equivalent isused as a common scale for measuring risk impact on various project success objectives,and the evidential reasoning (ER) algorithm is used as an assessment aggregation toolinstead of the simple averaging procedure which might not be appropriate in allsituations. A DST-based project evaluation framework was developed using projectrisks and benefits as evaluation attributes. Monetary equivalent was used also as acommon scale for measuring project risks and benefits and the ER algorithm as anaggregation tool.The viability of the proposed risk model, assessment methodology and projectevaluation framework was investigated through conducting interviews with constructionprofessionals and administering postal and online questionnaires. A decision supportsystem (DSS) was devised to facilitate the proposed approaches and to perform therequired calculations. The DSS was developed in light of the research findingsregarding the reasons of low take-up of the existing tools. Four validation case studieswere conducted. Senior managers in separate British construction companies tested thetool and found it useful, helpful and easy to use.It is concluded that the proposed risk model, risk assessment methodology and projectevaluation framework could be viable alternatives to the existing ones. Professionalexperience was modelled and utilised systematically for risk and benefit analysis. Thismay help closing the gap between theory and practice of risk analysis and decisionmaking in construction. The research findings recommend further exploration of thepotential applications of DST and ER in construction management domain.

Self-driving cars have recently become a challenging research topic, with the aim of making transportation safer and more efficient. Current advanced driving assistance systems (ADAS) allow cars to drive autonomously by following lane markings, identifying road signs and detecting pedestrians and other vehicles. In this thesis work we improve the robustness of autonomous cars by designing an on-road obstacle detection system. The proposed solution consists on the low-level fusion of radar and lidar through the occupancy grid framework. Two inference theories are implemented and evaluated: Bayesian probability theory and Dempster-Shafer theory of evidence. Obstacle detection is performed through image processing of the occupancy grid. Last, the Dempster-Shafer additional features are leveraged by proposing a sensor performance estimation module and performing advanced conflict management. The work has been carried out at Volvo Car Corporation, where real experiments on a test vehicle have been performed under different environmental conditions and types of objects. The system has been evaluated according to the quality of the resulting occupancy grids, detection rate as well as information content in terms of entropy. The results show a significant improvement of the detection rate over single-sensor approaches. Furthermore, the Dempster-Shafer implementation may slightly outperform the Bayesian one when there is conflicting information, although the high computational cost limits its practical application. Last, we demonstrate that the proposed solution is easily scalable to include additional sensors.

While exploring an unknown environment, an intelligent agent has only its sensors to guide its actions. Each sensor's ability to provide accurate information depends on the environment's characteristics. If the agent does not know these characteristics, how can it determine which sensors to rely on? This problem is exacerbated by sensing anomalies: cases where sensor(s) are working but the readings lead to an incorrect interpretation of the environment, e.g. laser sensors cannot detect glass. This work addresses the following research question: Can an inconsistency-based sensing accuracy indicator, which relies solely on fused sensor readings, be used to detect and characterize sensing anomalies in unknown environments? A novel inconsistency-based approach was investigated for sensing anomaly detection and characterization by a mobile robot using range sensing for mapping. Based on the hypothesis that sensing anomalies manifest as inconsistent sensor readings, the approach employed Dempster-Shafer theory and six metrics from the evidential literature to measure the magnitude of inconsistency. These were applied directly to fused sensor data with a threshold, forming an indicator, used to distinguish minor noise from anomalous readings. Experiments with real sensor data from four indoor and two outdoor environments showed that three of the six evidential inconsistency metrics can partially address the issue of noticing sensing anomalies in unknown environments. Polaroid sonar sensors, SICK laser range finders, and a Canesta range camera were used. Despite extensive training in known environments, the indicators could not reliably detect sensing anomalies, i.e. distinguish them from ordinary noise. However, sensing accuracy could be estimated (correlations with sensor error exceeded 0.8) and regions with suspect readings could be isolated. Trained indicators failed to rank sensors, but improved map quality by resetting suspect regions (up to 57.65%) or guiding sensor selection (up to 75.86%). This work contributes to the robotics and uncertainty in artificial intelligence communities by establishing the use of evidential metrics for adapting a single sensor or identifying the most accurate sensor to optimize the sensing accuracy in unknown environments. Future applications could enable intelligent systems to switch information sources to optimize mission performance and identify the reliability of sources for different environments.

yes / The orthocomplemented modular lattice of subspaces L[H(d)] , of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)] ). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H1,H2) , which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H1),P(H2) , to the subspaces H 1, H 2. As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities.

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Le calcul d'incidence généralisé est une technique hybride symbolique-numérique qui présente un potentiel intéressant pour la fusion de données, notamment par sa correspondance possible avec la théorie de l'évidence. Ce mémoire présente une série de modifications au calcul d'incidence généralisé afin qu'il puisse être utilisé pour éliminer le problème de bouclage d'information, un problème important de la fusion de données qui fait que les données corrélées prennent une importance plus grande. Ces modifications permettent aussi de représenter divers types de combinaisons à l'aide de l'approche des univers possibles. Il est notamment possible d'effectuer des combinaisons de Yager associatives et des parallèles peuvent être faits avec la théorie de Dezert et Smarandache. / Generalized Incidence Calculus is a hybrid symbolic-numeric approach to data fusion that presents many interesting characteristics, in particular a correspondence with the Theory of Evidence. This master's thesis presents modifications to Generalized Incidence Calculus for its application to eliminate the Data Looping problem which makes combination of correlated data take more importance. Those modifications also allow the representation of alternative combinations of the Theory of Evidence by using a possible worlds approach. In particular, it is possible to associatively combine data using the Yager combination and parallels can be made with the Dezert-Smarandache Theory.