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Integration of a cyber system and communication systems with the traditional power grid has enabled better monitoring and control of the smart grid making it more reliable and resilient. This empowers the system operators to make informed decisions as a result of better system visibility. The grid has moved from a completely air-gapped structure to a well-connected network. However, this remote-control capability to control distributed physical components in a distribution system can be exploited by adversaries with malicious intent to disrupt the power supply to the customers. Therefore, while taking advantage of the cyber-physical posture in the smart grid for improved controllability, there is a critical need for cybersecurity research to protect the critical power infrastructure from cyberattacks. While the literature regarding cybersecurity in distribution systems has focused on detecting and mitigating the cyberattack impact on the physical system, there has been limited effort towards a preventive approach for detecting cyberattacks. With this in mind, this dissertation focuses on developing intelligent solutions to detect cyberattacks in the cyber layer of the distribution grid and prevent the attack from impacting the physical grid. There has been a particular emphasis on the impact of coordinated attacks and the design of proactive defense to detect the attacker's intent to predict the attack trajectory. The vulnerability assessment of the cyber-physical system in this work identifies the key areas in the system that are prone to cyberattacks and failure to detect attacks timely can lead to cascading outages. A comprehensive cyber-physical system is developed to deploy different intrusion detection solutions and quantify the effect of proactive detection in the cyber layer. The attack detection approach is driven by artificial intelligence to learn attack patterns for effective attack path prediction in both a fully observable and partially observable distribution system. The role of effective communication technology in attack detection is also realized through detailed modeling of 5G and latency requirements are validated. / Doctor of Philosophy / The traditional power grid was designed to supply electricity from the utility side to the customers. This grid model has shifted from a one-directional supply of power to a bi-directional one where customers with generation capacity can provide power to the grid. This is possible through bi-directional data flow which ensures the complete power system observability and allows the utility to monitor and control distributed power components remotely. This connectivity depends on the cyber system and efficient communication for ensuring stable and reliable system operations. However, this also makes the grid vulnerable to cyberattacks as the traditional air-gapped grid has evolved into a highly connected network, thus increasing the attack surface for attackers. They might pose the capability to intrude on the network by exploiting network vulnerability, move laterally through different aspects of the network, and cause operational disruption. The type of disruption can be minor voltage fluctuations or even widespread power outages depending on the ultimate malicious attack goal of such adversaries. Therefore, cybersecurity measures for protecting critical power infrastructure are extremely important to ensure smooth system operations. There has been recent research effort for detecting such attacks, isolating the attacked parts in the grid, and mitigating the impact of the attack, however, instead of a passive response there is a need for a preventive or proactive detection mechanism. This can ensure capturing the attack at the cyber layer before intruders can impact the physical grid. This is the primary motivation to design an intrusion detection system that can detect different coordinated attacks (where different attacks are related and directed towards a specific goal) and can predict the attack path. This dissertation focuses on first identifying the vulnerabilities in the distribution system and a comprehensive cyber-physical system is developed. Different detection algorithms are developed to detect cyberattacks in the distribution grid and have the intelligence to learn the attack patterns to successfully predict the attack path. Additionally, the effectiveness of advanced communication such as 5G is also tested for different system operations in the distribution system.

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.

Abductive reasoning is the process of finding a best explanation for a set of observations. In many abductive problems, like medical diagnosis, scientific discovery, debugging or troubleshooting, an amount of information far beyond the capacity limits of working memory (WM) must be processed. Although WM plays a central role in theories of human cognition, theories of abductive reasoning do not specify WM processes during the generation of explanations. On the basis of a computational model of abductive reasoning and of theories of text comprehension a mechanism is proposed that reduces WM load during abductive reasoning. The computational model views abductive reasoning as the sequential comprehension and integration of observations into a situation model that represents the current best explanation for the observations. The proposed WM mechanism assumes that the situation model is only partly kept in WM, whereas other pieces are stored in long-term memory. These long-term representation part can be reliably accessed through retrieval structures to reinstatiate information in WM during abductive reasoning. It is assumed that unexplained observations are actively maintained in WM until an explanation for them could be generated. Thereafter their representation is lost from WM. But these explained observations can be recalled from long-term memory via their integration into the situation model. This mechanism makes predictions about the availability of the mental representation of explained and unexplained observations. These predictions were tested in four experiments, using different memory tests for observations. In Experiments 1 and 2 a recognition test was used, in Experiment 3 an implicit menory test was used and in Experiment 4 the participants had to perform an unexpected recall after task interruption. The results show that unexplained observations are accessed faster than explained ones during abductive reasoning. This confirms the mechanism's assumption that unexplained observations are kept in WM and explained ones not. But explained observations seem not to be represented in long-term memory. Rather, it seems that observations are rapidly forgotten afer they are explained. Different possible reasons for this pattern of result are discussed.

Abductive reasoning is the process of finding the best explanation for a set of observations. The theory of abductive reasoning (TAR, Johnson & Krems, 2001) allows detailed process assumptions that were only partly tested in detail up until now. This thesis employs an artificial abductive reasoning task, the Black Box task, and eye tracking measures in order to gain insight into the process. The first part of this thesis aims at evaluating process measures based on eye tracking and using them in order to gain a better understanding of the processes postulated in TAR such as the construction of a situation model or retrieval of relevant information. The second part investigates the relationship between working memory and abductive reasoning by manipulating the amount of information stored in memory and examining the relationship between visual abductive reasoning and working memory skills. In a last part a perspective to the transferability of our results to everyday life tasks is given. The first study focuses on differentiating between processes that take place during the encoding and the evaluation of observation information by comparing eye movement measures. In the second study, we tested process assumptions such as the construction of a mental representation from TAR using memory indexing, an eye tracking method that makes it possible to trace the retrieval of explanations currently held in working memory. Gaze analysis revealed that participants encode the presented evidence (i.e., observations) together with possible explanations into memory. When new observations are presented, the previously presented evidence and explanations are retrieved. With the memory indexing method, we were able to assess the process of information retrieval in abductive reasoning, which was previously believed to be unobservable. The theory of abductive reasoning (TAR; Johnson & Krems, 2001) assumes that when information is presented sequentially, new information is integrated into a mental representation called a situation model, the central data structure on which all reasoning processes are based. Since working memory capacity is limited, the question arises how reasoning might change with the amount of information that has to be processed in memory. To answer this question, we conducted a third experimental study, in which we manipulated whether previous observation information and previously found explanations had to be retrieved from memory or were still present in the visual array. We analyzed individual ratings of difficulty as well as behavioral data and reasoning outcomes. Our results provide evidence that people experience differences in task difficulty when more information has to be retrieved from memory. This is also evident in changes in the mental representation as reflected by eye tracking measures. However, these differences are not evident in the reasoning outcome. These findings suggest that individuals construct their situation model from both information in memory as well as external memory stores. The complexity of the model depends on the task at hand: when memory demands are high, only relevant information is included. With this compensation strategy, people are able to achieve similar reasoning outcomes even when faced with more difficult tasks. The precise relationship between reasoning and working memory capacity remains largely opaque. Combining data of both studies from chapter 3 and 4, we firstly investigated if reasoning performance differs due to differences in working memory capacity. Secondly, using eye tracking, we explored the relationship between the facets of working memory and the process of visuospatial reasoning. Therefore both, a test for storage and processing, and content components (verbal-numerical/ spatial) of working memory as well as an intelligence measure, were engaged. Results show a clear relationship between reasoning accuracy, spatial storage and processing components as well as intelligence. Process measures suggest that high spatial working memory ability might lead to the use of strategies optimizing the content and complexity of the mental representation on which abductive reasoning is based. In a fifth study, we aimed to investigate whether there are also indicators for the mechanisms postulated by TAR in a task that is closer to real life reasoning. Therefore, we asked participants to solve 12 jigsaw puzzles whereby the abductive task was the identification of the motive presented on the puzzles. Thereby, the pieces of the puzzles posed as observation and hypotheses to the motive of the puzzle as explanations. As a process tracing measure, we used thinking aloud. Verbal protocols were recorded, transcripted and carefully coded according to the operators and explanation types postulated in TAR. We found evidence that participants use most of the operators with a likeliness that significantly lies above chance level. We also found evidence of the existence of the different explanation types. Eye movements were able to give insight in the interrelations between working memory, attention, and action. Therefore, this work contributes to understanding abductive reasoning, not only by testing the assumptions of TAR, but also by finding relations between memory, action and thought. The results do not only account for abductive reasoning in an artificial task but also in everyday life reasoning.:1 Introduction 1 1.1 Theories on Abductive Reasoning and Beyond 4 1.1.1 Theory of Abductive Reasoning 4 1.1.2 Other Theories 7 1.2 Reasoning, Working Memory, and Mental Representation 9 1.3 Process Tracing 11 1.4 An Artificial Abductive Task: The Black Box 12 1.5 Overview and Research Objectives 15 1.5.1 Differentiating between Encoding and Processing 15 1.5.2 Current Explanations in Memory 16 1.5.3 Information Stored in Memory 16 1.5.4 More than Storage of Information 17 1.5.5 In the Context of Everyday Life 18 1.5.6 Summary, Perspectives, and Conclusion 18 2 The Possibilities of Eye Tracking: Differentiating between Encoding and Processing 21 2.1 Abstract 22 2.2 Introduction 23 2.3 Method 26 2.3.1 Participants 26 2.3.2 Task and Apparatus 27 2.3.3 Procedure 28 2.3.4 Analysis 29 2.4 Results 30 2.5 Discussion 32 3 Tracing Current Explanations in Memory: A Process Analysis Based on Eye Tracking 37 3.1 Abstract 38 3.2 Introduction 39 3.2.1 Current Explanations of Abductive Reasoning 41 3.2.2 Tracing the Reasoning Process 44 3.2.3 Present Study 45 3.3 Method 48 3.3.1 Participants 49 3.3.2 Apparatus 49 3.3.3 Material 50 3.3.4 Procedure 53 3.4 Results 54 3.4.1 Performance 54 3.4.2 Gaze Analyses 55 3.4.3 Hypothesis 1: Information Stored in the Situation Model 57 3.4.4 Hypothesis 2: Different Types of Explanations—Concrete vs. Abstract 61 3.5 Discussion 67 3.5.1 Information Stored in the Situation Model 68 3.5.2 Concretely and Abstractly Explained Observations 68 3.5.3 TAR and Current Theories on Abductive Reasoning 70 3.5.4 Tracing Memory Processes 72 3.5.5 Conclusion 74 Appendix 3.1 75 Appendix 3.2 76 Appendix 3.3 77 Appendix 3.4 78 4 Information Stored in Memory Affects Abductive Reasoning 79 4.1 Abstract 80 4.2 Introduction 81 4.2.1 The Reasoning Process 82 4.2.2 Visual Attention 85 4.2.3 Research Objectives 86 4.2.4 This Study 87 4.2.5 Using Eye Movements as a Method to Assess Memory Retrieval 89 4.2.6 Hypotheses 89 4.3 Method 92 4.3.1 Participants 92 4.3.2 Apparatus 92 4.3.3 The Black Box Task 92 4.3.4 Procedure 95 4.3.5 Pairwise Comparisons 96 4.4 Results 96 4.4.1 Performance 96 4.4.2 Gaze Analysis 99 4.4.3 Hypothesis 1: Differences Experienced in Task Difficulty 101 4.4.4 Hypothesis 2: Elements of the Situation Model 102 4.4.5 Hypothesis 3: Integrative Solutions 105 4.5 Discussion 107 4.5.1 Differences Experienced in Task Difficulty 108 4.5.2 Elements of the Situation Model 108 4.5.3 Integrative Solutions 110 4.5.4 Summary 112 5 More than Storage of Information – What Working Memory Contributes to Visual Abductive Reasoning 113 5.1 Abstract 114 5.2 Introduction 115 5.2.1 Working memory 116 5.2.2 Relations between Abductive Reasoning Working Memory Capacity 118 5.2.3 Eye Movements as a Process Tracing Method 119 5.2.4 Abductive Reasoning Outcomes and Working Memory Ability. 120 5.2.5 Abductive Reasoning Processes and Working Memory Ability 121 5.3 Method 123 5.3.1 Participants 124 5.3.2 Apparatus 124 5.3.3 Material 125 5.3.4 Procedure 127 5.4 Results 128 5.4.1 Analysis 128 5.4.2 Abductive Reasoning Accuracy and Working Memory Ability 131 5.4.3 Abductive Reasoning Processes and Working Memory Ability 132 5.5 Discussion 135 5.5.1 The Interaction of Reasoning Accuracy and Memory Ability 135 5.5.2 The Interaction of the Process of Reasoning and Memory Ability 136 5.5.3 Conclusion 138 6 The Theory of Abductive Reasoning in the Context of Everyday Life 141 6.1 Abstract 142 6.2 Introduction 143 6.2.1 Abduction in “Real Life” 145 6.3 Method 146 6.3.1 Participants 146 6.3.2 Task 147 6.3.3 Material 148 6.3.4 Apparatus 148 6.3.5 Procedure 149 6.3.6 Coding system 150 6.4 Results 153 6.4.1 Analysis 153 6.4.2 Descriptive Data 153 6.3.3. Likeliness of Operator Use 155 6.5 Discussion 156 6.5.1 Operator Use 156 6.5.2 Explanation Types 157 6.5.3 Perspectives 158 7 Summary, Perspectives, and Conclusion 159 7.1 The Process of Abductive Reasoning 159 7.2 Contributions of other Theories 162 7.3 Eye Tracking and its Methodological Implications 164 7.4 Future Research and Applications 167 7.5 Conclusion 169 8 References 171 Curriculum Vitae 191 Publications 196

Introduction : Établir un diagnostic médical fiable requiert l’identification de la maladie d’un patient sur la base de l’observation de ses signes et symptômes. Par ailleurs, les ontologies constituent un formalisme adéquat et performant de représentation des connaissances biomédicales. Cependant, les ontologies classiques ne permettent pas de représenter les connaissances liées au processus du diagnostic médical : connaissances probabilistes et connaissances imprécises et vagues. Matériel et méthodes : Nous proposons des méthodes générales de représentation des connaissances afin de construire des ontologies adaptées au diagnostic médical. Ces méthodes permettent de représenter : (a) Les connaissances imprécises et vagues par la discrétisation des concepts (définition de plusieurs catégories distinctes à l’aide de valeurs seuils ou en représentant les différentes modalités possibles). (b) Les connaissances probabilistes (les sensibilités et les spécificités des signes pour les maladies, et les prévalences des maladies pour une population donnée) par la réification des relations ayant des arités supérieures à 2. (c) Les signes absents par des relations et (d) les connaissances liées au processus du diagnostic médical par des règles SWRL. Un moteur d’inférences abductif et probabiliste a été conçu et développé. Ces méthodes ont été testées à l’aide de dossiers patients réels. Résultats : Ces méthodes ont été appliquées à trois domaines (les maladies plasmocytaires, les urgences odontologiques et les lésions traumatiques du genou) pour lesquels des modèles ontologiques ont été élaborés. L’évaluation a permis de mesurer un taux moyen de 89,34% de résultats corrects. Discussion-Conclusion : Ces méthodes permettent d’avoir un modèle unique utilisable dans le cadre des raisonnements abductif et probabiliste, contrairement aux modèles proposés par : (a) Fenz qui n’intègre que le mode de raisonnement probabiliste et (b) García-crespo qui exprime les probabilités hors du modèle ontologique. L’utilisation d’un tel système nécessitera au préalable son intégration dans le système d’information hospitalier pour exploiter automatiquement les informations du dossier patient électronique. Cette intégration pourrait être facilitée par l’utilisation de l’ontologie du système. / Introduction: Making a reliable medical diagnosis requires the identification of the patient’s disease based on the observation of signs. Moreover, ontologies provide an adequate and efficient formalism for medical knowledge representation. However, classical ontologies do not allow representing knowledge associated with medical reasoning such as probabilistic, imprecise, or vague knowledge. Material and methods: In the current work, general knowledge representation methods are proposed. They aim at building ontologies fitting to medical diagnosis. They allow to represent: (a) imprecise or vague knowledge by discretizing concepts (definition of several distinct categories thanks to threshold values or by representing the various possible modalities), (b) probabilistic knowledge (sensitivity, specificity and prevalence) by reification of relations of arity greater than 2, (c) absent signs by relations and (d) medical reasoning and reasoning on the absent signs by SWRL rules. An abductive reasoning engine and a probabilistic reasoning engine were designed and implemented. The methods were evaluated by use of real patient records. Results: These methods were applied to three domains (the plasma cell diseases, the dental emergencies and traumatic knee injuries) for which the ontological models were developed. The average rate of correct diagnosis was 89.34 %. Discussion-Conclusion: In contrast with other methods proposed by Fenz and García-crespo, the proposed methods allow to have a unique model which can be used both for abductive and probabilistic reasoning. The use of such a system will require beforehand its integration in the hospital information system for the automatic exploitation of the electronic patient record. This integration might be made easier by the use of the ontology on which the system is based.

Sustainable performance of buildings has become a major concern among construction industry professionals. However, sustainability considerations are often treated as an add-on to building design, following ad hoc processes for their implementation. As a result, the most common problem to achieve a sustainable building outcome is the absence of the right information at the right time to make critical decisions. For design team members to appreciate the requirements of multidisciplinary collaboration, there is a need for transparency and a shared understanding of the process. The aim of this study is to investigate, model, and facilitate the early stages of Building Information Modelling (BIM) enabled Sustainable Building Design (SBD) by formalising the ad hoc working relationships of the best practices in order to standardise the optimal collaboration workflows. Thus, this research strives to improve BIM maturity level for SBD, assisting in the transition from ad hoc to defined , and then, to managed . For this purpose, this study has adopted an abductive research approach (iterative process of induction and deduction) for theory building and testing. Four (4) stages of data collection have been conducted, which have resulted in a total of 32 semi-structured interviews with industry experts from 17 organisations. Fourteen (14) best practice case studies have been identified, and 20 incidents narratives have been collected applying the Critical Decision Method (CMD) to examine roles and responsibilities, resources, information exchanges, interdependencies, timing and sequence of events, and critical decisions. As a result, the research has classified the critical components of SBD into a framework utilising content and thematic analyses. These have included the definition of roles and competencies that are essential for SBD along with the existing opportunities, challenges, and limitations. Then, Schedules of Services for SBD have been developed for the following stages of the RIBA Plan of Work 2013: stage 0 (Strategic Definition), stage 1 (Preparation and Brief), and stage 2 (Concept Design). The abovementioned SBD components have been coordinated explicitly into a systematic process, which follows Concurrent Engineering (CE) principles utilising Integrated DEFinition (IDEF) structured diagramming techniques (IDEF0 and IDEF3). The results have identified the key players roles and responsibilities, tasks (BIM Uses), BIM-based deliverables, and critical decision points for SBD. Furthermore, Green BIM Box (GBB) workflow management prototype tool has been developed to analyse communication and delivery of BIM-enabled SBD in a centralised system (Common Data Environment, CDE). GBB s system architecture for SBD process automation is demonstrated through Use Case Scenarios utilising the OMG UML (Object Management Group s Unified Modelling Language) notation. The proposed solution facilitates the implementation of BIM, Information Communication Technology (ICT), and Building Performance Analysis (BPA) software to realise the benefits of combining distributed teams expertise holistically into a common process. Finally, the research outcomes have been validated through academic and industrial reviews that have led to the refinement of the IDEF process model and framework. It has been found that collaborative patterns are repeatable for a variety of different non-domestic building types such as education, healthcare, and offices. Therefore, the research findings support the idea that a detailed process, which follows specified communication patterns, can assist in achieving sustainability targets efficiently in terms of time, cost, and effort.