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Yes / Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.

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. / Ph. D.

This dissertation investigates algorithmic techniques for temporal process discovery in many domains. Many different formalisms have been proposed for modeling temporal processes such as motifs, dynamic Bayesian networks and partial orders, but the direct inference of such models from data has been computationally intensive or even intractable. In this work, we propose the mining of frequent episodes as a bridge to inferring more formal models of temporal processes. This enables us to combine the advantages of frequent episode mining, which conducts level wise search over constrained spaces, with the formal basis of process representations, such as probabilistic graphical models and partial orders. We also investigate the mining of frequent episodes in infinite data streams which further expands their applicability into many modern data mining contexts. To demonstrate the usefulness of our methods, we apply them in different problem contexts such as: sensor networks in data centers, multi-neuronal spike train analysis in neuroscience, and electronic medical records in medical informatics. / Ph. D.

Yes / Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system. / The research of Sohag Kabir was partly funded by the DEIS project (Grant Agreement 732242).

Yes / In different engineering processes, the reliability of systems is increasingly evaluated to ensure that the safety-critical process systems will operate within their expected operational boundary for a certain mission time without failure. Different methodologies used for reliability analysis of process systems include Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Bayesian Networks (BN). Although these approaches have their own procedures for evaluating system reliability, they rely on exact failure data of systems’ components for reliability evaluation. Nevertheless, obtaining exact failure data for complex systems can be difficult due to the complex behaviour of their components, and the unavailability of precise and adequate information about such components. To tackle the data uncertainty issue, this chapter proposes a framework by combining intuitionistic fuzzy set theory and expert elicitation that enables the reliability assessment of process systems using FTA. Moreover, to model the statistical dependencies between events, we use the BN for robust probabilistic inference about system reliability under different uncertainties. The efficiency of the framework is demonstrated through application to a real-world system and comparison of the results of analysis produced by the existing approaches. / The full text will be available at the end of the publisher's embargo, 9th April 2025

Agriculture 5.0, marked by advanced technology and intensified human-machine collaboration, addresses significant challenges in traditional farming, such as labor shortages, declining productivity, climate change impacts, and gender disparities. This study assesses the effectiveness of probabilistic machine learning methods, with a specific focus on Bayesian networks (BN), collaborative filtering (CF), and fuzzy cognitive map (FCM) techniques, in enhancing cybersecurity risk analysis and management in Agriculture 5.0. It also explores unique cybersecurity threats within Agriculture 5.0. Using a systematic literature review (SLR), and leveraging historical data, case studies, experimental datasets, probabilistic machine learning algorithms, experiments, expert insights, and data analysis tools, the study evaluates the effectiveness of these techniques in improving cybersecurity risk analysis in Agriculture 5.0. BN, CF, and FCM were found effective in enhancing the cybersecurity of Agriculture 5.0. This research enhances our understanding of how probabilistic machine learning can bolster cybersecurity within Agriculture 5.0. The study's insights will be valuable to industry stakeholders, policymakers, and cybersecurity professionals, aiding the protection of agriculture's digital transformation amid increasing technological complexity and cyber threats, and setting the stage for future investigations into Agriculture 5.0 security.

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Ces dernières années, les attaques de pirates contre des navires ou des champs pétroliers n'ont cessé de se multiplier et de s'aggraver. Pour exemple, l'attaque contre la plateforme Exxon Mobil en 2010 au large du Nigeria s'est soldée par l'enlèvement de dix-neuf membres d'équipage et la réduction de 45.000 barils de sa production pétrolière quotidienne ce qui a engendré une montée des prix à l'échelle internationale.Cet exemple est une parfaite illustration de l'ampleur des dommages sur la sécurité des infrastructures pétrolières offshore.Dans le cadre de notre recherche, nous proposons une démarche de pilotage et de management du risque de piraterie en se basant sur le concept des réseaux bayésiens qui permettent la représentation des connaissances et le calcul des probabilités conditionnelles.Une dimension temporelle a été ajoutée par le recours aux réseaux bayésiens qualifiés de « dynamiques ». Ces réseaux, fondés sur les chaines de Markov cachées ou filtres de Kalman, se révèlent très performants dans le domaine de l'analyse des risques.L'application de ces réseaux au domaine de la piraterie a été envisagée, les apports et les limites seront évalués dans le cadre de cette thèse. / In recent years, pirate attacks against ships or oil fields have continued to multiply and worsen. For example, the attack against the Exxon Mobil platform in 2010 in the coast of Nigeria has resulted in the removal of nineteen crew members and the reduction of 45,000 barrels of daily oil production which resulted in a rise prices internationally.This example is a perfect illustration of the extent of damage on the safety of offshore oil infrastructure.As part of our research, we propose an approach to control and management of the risk of piracy based on the concept of Bayesian networks that enable knowledge representation and calculation of conditional probabilities.A temporal dimension was added by the use of Bayesian networks called "dynamic". These networks, based on Markov chains hidden or Kalman filters, are proving very effective in the field of risk analysis.The application of these networks on piracy was considered, the contributions and limitations will be evaluated as part of this thesis.