The object of this thesis is proposal and practical implementation of expert system, whose knowledge base will be modeling by fuzzy Petri nets. The proposal is based on knowledge in theoretical analysis of diagnostic expert system and fuzzy Petri nets. This proposal is realised in programming language C#. There are described functions of program and it is made a model consultation with using two different knowledge base.

This master thesis contains the basic information about knowledge and expert systems. The thesis contains theoretic text about architecture of the expert systems and representation knowledge. The text regarding on representation knowledge contains examples of different ways of knowledge representation for expert systems. In the next part is described the design and all functions of the expert systems. This expert system uses frames representation.

Cette thèse présente un modèle résilient pour piloter un avion basé sur une logique non monotone. Ce modèle est capable de gérer des solutions à partir d’informations incomplètes, contradictoires et des exceptions. C’est un problème très connu en Intelligence Artificial, qui est étudié depuis plus de 40 ans. Pour ce faire, nous utilisons la logique des défauts pour formaliser la situation et trouver des conclusions possibles. Grâce à cette logique, nous pouvons transformer les règles de pilotage en défauts. Ensuite, lorsque nous calculons les solutions, plusieurs options peuvent en résulter. À ce stade, il existe un critère de décision opportuniste pour choisir la meilleure solution. Le contrôle du système se fait via la propriété de résilience. Nous redéfinissons cette propriété comme l’intégration de la logique non monotone dans le modèle de Minsky. En conséquence, il est démontré que le modèle de résilience proposé pourrait être généralisé aux systèmes intégrant une connaissance du monde contenant des situations, des objectifs et des actions. Enfin, nous présentons les résultats expérimentaux et la conclusion de la thèse en discutant des perspectives et des défis pour les orientations futures. Différentes applications dans d’autres domaines sont prises en compte pour l’intérêt du comportement du modèle. / This thesis presents a resilient model to pilot an aircraft based on a non-monotonic logic. This model is capable of handling solutions from incomplete, contradictory information and exceptions. This is a very well known problem in Artificial Intelligence, which has been studied for more than 40 years. To do this, we use default logic to formalise the situation and find possible conclusions. Thanks to this logic we can transform the piloting rules to defaults. Then, when we calculate the solutions, several options could result. At this point an opportunistic decision criteria takes place to choose the better solution. The control of the system is done via the property of resilence, we redefine this property as the integration of the non-monotonic logic in the Minsky’s model. As a result, it is shown that the proposed resilient model could be generalised to systems that incorporate a knowledge of the world that contains situations, objectives and actions. Finally, we present the experimental results and conclusion of the thesis discussing the prospects and challenges that exist for future directions. Different applications in other fields are taken into account for the interest of the model’s behavior.

(in English): The thesis investigates knowledge management in the context of information science and human resources management strategy. It includes (1) analysis of the current status of knowledge management and the potential of knowledge management to be evaluated using audit procedures, (2) comparative analysis of knowledge management as approached in the Czech Republic and other countries, and (3) knowledge management audit proposal.

<div>The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of ride-sharing platforms and increased research on using autonomous vehicle technologies for routing and matching. The future of urban mobility for passengers and goods relies on leveraging new methods that minimize operational costs and environmental footprints of transportation systems. </div><div><br></div><div>This paper considers combining passenger transportation with goods delivery to improve vehicle-based transportation. Even though the problem has been studied with model-based approaches where the dynamic model of the transportation system environment is defined, model-free approaches where the dynamics of the environment are learned by interaction have been demonstrated to be adaptable to new or erratic environment dynamics. </div><div><br></div><div>FlexPool is a distributed model-free deep reinforcement learning algorithm that jointly serves passengers \& goods workloads by learning optimal dispatch policies from its interaction with the environment. The model-free algorithm (as opposed to a model-based one) is an algorithm which does not use the transition probability distribution (and the reward function) associated with the Markov decision process (MDP).</div><div> The proposed algorithm pools passengers for a ride-sharing service and delivers goods using a multi-hop routing method. These flexibilities decrease the fleet's operational cost and environmental footprint while maintaining service levels for passengers and goods. The dispatching algorithm based on deep reinforcement learning is integrated with an efficient matching algorithm for passengers and goods. Through simulations on a realistic urban mobility platform, we demonstrate that FlexPool outperforms other model-free settings in serving the demands from passengers \& goods. FlexPool achieves 30\% higher fleet utilization and 35\% higher fuel efficiency in comparison to (i) model-free approaches where vehicles transport a combination of passengers \& goods without the use of multi-hop transit, and (ii) model-free approaches where vehicles exclusively transport either passengers or goods. </div>

The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model.

Facing the growing amount of information in today's society, the task of specifying human knowledge in a way that can be unambiguously processed by computers becomes more and more important. Two acknowledged fields in this evolving scientific area of Knowledge Representation are Description Logics (DL) and Formal Concept Analysis (FCA). While DL concentrates on characterizing domains via logical statements and inferring knowledge from these characterizations, FCA builds conceptual hierarchies on the basis of present data. This work introduces Relational Exploration, a method for acquiring complete relational knowledge about a domain of interest by successively consulting a domain expert without ever asking redundant questions. This is achieved by combining DL and FCA: DL formalisms are used for defining FCA attributes while FCA exploration techniques are deployed to obtain or refine DL knowledge specifications.

Description Logics (DLs) are a class of knowledge representation formalisms that can represent terminological and assertional knowledge using a well-defined semantics. Often, knowledge engineers are experts in their own fields, but not in logics, and require assistance in the process of ontology design. This thesis presents three methods that can extract terminological knowledge from existing data and thereby assist in the design process. They are based on similar formalisms from Formal Concept Analysis (FCA), in particular the Next-Closure Algorithm and Attribute-Exploration. The first of the three methods computes terminological knowledge from the data, without any expert interaction. The two other methods use expert interaction where a human expert can confirm each terminological axiom or refute it by providing a counterexample. These two methods differ only in the way counterexamples are provided.

Die hier zur Verfügung gestellte Datei ist leider nicht vollständig, aus technischen Gründen sind die folgenden Anhänge leider nicht enthalten: Anhang 3: Begriffshierarchie "verarbeitungstechnische Funktion" S. 141 Anhang 4: Begriffshierarchie "Eigenschaftsänderung" S. 144 Anhang 5: Begriffshierarchie "Verarbeitungsgut" S. 149 Anhang 6: Begriffshierarchie "Verarbeitungstechnisches Prinzip" S. 151 Konsultieren Sie die Druckausgabe, die Sie im Bestand der SLUB Dresden finden: http://slubdd.de/katalog?TN_libero_mab21079933:ABKÜRZUNGEN UND FORMELZEICHEN S. 5 1. EINLEITUNG S. 7 2. UNTERSTÜTZUNGSMITTEL FÜR DIE KONZEPTPHASE IN DER VERARBEITUNGSMASCHINEN-KONSTRUKTION - ALLGEMEINE ANFORDERUNGEN, ENTWICKLUNGSSTAND 9 2.1. DIE BEDEUTUNG DER KONZEPTPHASE IN DER VERARBEITUNGSMASCHINENKONSTRUKTION S. 9 2.2. ALLGEMEINE ANFORDERUNGEN AN UNTERSTÜTZUNGSMITTEL FÜR DEN KONSTRUKTEUR ALS PROBLEMLÖSER S. 13 2.3. SPEZIFIK VERARBEITUNGSTECHNISCHER PROBLEMSTELLUNGEN S. 17 2.3.1. Verarbeitungstechnische Informationen im Konstruktionsprozeß von Verarbeitungsmaschinen S. 17 2.3.2. Komplexität verarbeitungstechnischer Probleme S. 19 2.3.3. Unbestimmtheit verarbeitungstechnischer Probleme S. 21 2.3.4. Beschreibungsspezifik verarbeitungstechnischer Problemstellungen S. 22 2.4. UNTERSTÜTZUNGSMITTEL FÜR DIE KONZEPTPHASE UND IHRE EIGNUNG FÜR DIE VERARBEITUNGSMASCHINENKONSTRUKTION S. 24 2.4.1. Traditionelle Unterstützungsmittel für die Lösungssuche S. 24 2.4.1.1. Lösungskataloge S. 24 2.4.1.2. Konstruktionsmethodik in der Prinzipphase S. 25 2.4.2. Rechnerunterstützung für die Konstruktion mit Relevanz für die Konzeptphase S. 28 2.4.2.1. Kurzüberblick über Konstruktionsunterstützungssysteme und ihre Einbindung in übergeordnete Systeme S. 28 2.4.2.2. Rechnerunterstützung zum Analysieren S. 31 2.4.2.3. Rechnerunterstützung zum Informieren S. 32 2.4.2.4. Rechnerunterstützung zum Synthetisieren S. 34 2.4.2.5. Rechnerunterstützung zum Bewerten und Auswählen S. 39 2.4.2.6. Integrierende Systeme mit Unterstützung für die Konzeptphase S. 41 2.4.3. Der Wissensspeicher Verarbeitungstechnik S. 43 2.5. SCHLUßFOLGERUNGEN AUS DER ANALYSE DES IST-STANDES S. 46 3. ANFORDERUNGEN AN EINE RECHNERUNTERSTÜTZUNG DER PRINZIPPHASE DER VERARBEITUNGSMASCHINENKONSTRUKTION 47 3.1. FUNKTIONSBESTIMMUNG S. 47 3.1.1. Typisierung der mit dem System zu lösenden Fragestellungen S. 47 3.1.2. Anforderungen an Funktionalität und Dialoggestaltung S. 50 3.2. INHALTLICHE ABGRENZUNG S. 54 3.3. ANFORDERUNGEN AN DIE WISSENSREPRÄSENTATION S. 57 4. INFORMATIONSMODELL DES VERARBEITUNGSTECHNISCHEN PROBLEMRAUMES S. 61 4.1. ÜBERBLICK ÜBER MÖGLICHE DARSTELLUNGSARTEN S. 61 4.1.1. Allgemeiner Überblick S. 61 4.1.1.1. Unterschiede zwischen wissensbasierten Systemen und anderen Wissensrepräsentationsformen S. 61 4.1.1.2. Algorithmische Modellierung S. 62 4.1.1.3. Relationale Modellierung S. 63 4.1.1.4. Darstellungsformen in wissensbasierten Systemen S. 64 4.1.2. Die verwendete Software und ihre Möglichkeiten S. 71 4.2. ÜBERBLICK ÜBER DEN SYSTEMAUFBAU S. 74 4.2.1. Gesamtüberblick S. 74 4.2.2. Sichtenmodell S. 78 4.2.3. Relationale Darstellung von Prinzipinformationen, Kennwerten und Kenngrößen S. 83 4.2.4. Bildinformationen S. 85 4.2.5. Ergänzende Informationen in der Benutzeroberfläche S. 86 4.3. MODELLIERUNG VON WISSENSKOMPONENTEN DER DOMÄNE VERARBEITUNGSTECHNIK S. 87 4.3.1. Abbildung verarbeitungstechnischer Funktionen S. 87 4.3.1.1. Darstellungsarten für verarbeitungstechnische Funktionen - Bedeutung, Verwendung, Probleme S. 87 4.3.1.2. Die Sicht "Verarbeitungstechnische Funktion" S. 89 4.3.1.3. Die Sicht "Eigenschaftsänderung" S. 90 4.3.2. Abbildung von Informationen über Verarbeitungsgüter S. 93 4.3.2.1. Beschreibungskomponenten und ihre Verwendung bei der Lösungssuche S. 93 4.3.2.2. Die Sicht "Verarbeitungsgut" S. 94 4.3.2.3. Abbildung von Verarbeitungsguteigenschaften S. 94 4.3.3. Abbildung verarbeitungstechnischer Prinzipe S. 96 4.3.3.1. Die Sicht "Verarbeitungstechnisches Prinzip" S. 96 4.3.3.2. Die Detailbeschreibung verarbeitungstechnischer Prinzipe S. 97 4.3.4. Verarbeitungstechnische Kenngrößen S. 99 4.3.5. Darstellung von Zusammenhängen mittels Regeln S. 100 4.3.6. Unterstützung der Feinauswahl S. 102 5. PROBLEMLÖSEN MIT DEM BERATUNGSSYSTEM VERARBEITUNGSTECHNIK S. 104 5.1. INTERAKTIVE PROBLEMAUFBEREITUNG S. 104 5.2. BESTIMMUNG DER LÖSUNGSMENGE - GROBAUSWAHL S. 109 5.3. FEINAUSWAHL S. 110 5.4. VERARBEITUNG DER ERGEBNISSE S. 112 6. WISSENSAKQUISITION S. 113 6.1. PROBLEME BEI DER WISSENSAKQUISITION S. 113 6.2. VORSCHLÄGE ZUR UNTERSTÜTZUNG UND ORGANISATION DER AKQUISITION FÜR DAS BERATUNGSSYSTEM VERARBEITUNGSTECHNIK S. 115 7. GEDANKEN ZUR WEITERENTWICKLUNG S. 116 7.1. INHALTLICHER UND FUNKTIONALER AUSBAU DES BERATUNGSSYSTEMS VERARBEITUNGSTECHNIK S. 116 7.1.1. Ergänzung der Sichtenbeschreibung durch weitere Sichten S. 116 7.1.2. Andere Erweiterungsmöglichkeiten S. 117 7.2. EINBINDUNGSMÖGLICHKEITEN FÜR DAS BERATUNGSSYSTEMS VERARBEITUNGSTECHNIK S. 118 8. ZUSAMMENFASSUNG S. 120 LITERATURVERZEICHNIS S. 123 Anhang 1: Beispiele für phasenübergreifende Rechnerunterstützung der Konstruktion 134 Anhang 2: Inhalt der Kerntabelle "Prinzip" S. 138 Anhang 3: Begriffshierarchie "verarbeitungstechnische Funktion" S. 141 Anhang 4: Begriffshierarchie "Eigenschaftsänderung" S. 144 Anhang 5: Begriffshierarchie "Verarbeitungsgut" S. 149 Anhang 6: Begriffshierarchie "Verarbeitungstechnisches Prinzip" S. 151 Anhang 7: Implementierung einer umstellbaren Formel am Beispiel Dichteberechnung S. 158

A key component of research in the aerospace industry constitutes hypersonic flights (M>5) which includes the design of commercial high-speed aircrafts and development of rockets. Computational analysis becomes more important due to the difficulty in performing experiments and reliability of its results at these harsh operating conditions. There is an increasing demand from the industry for the accurate prediction of wall-shear and heat transfer with a low computational cost. Direct Numerical Simulations (DNS) create the standard for accuracy, but its practical usage is difficult and limited because of its high cost of computation. The usage of Reynold's Averaged Navier Stokes (RANS) simulations provide an affordable gateway for industry to capitalize its lower computational time for practical applications. However, the presence of existing RANS turbulence closure models and associated wall functions result in poor prediction of wall fluxes and inaccurate solutions in comparison with high fidelity DNS data. In recent years, machine learning emerged as a new approach for physical modeling. This thesis explores the potential of employing Machine Learning (ML) to improve the predictions of wall fluxes for hypersonic turbulent boundary layers. Fine-grid RANS simulations are used as training data to construct a suitable machine learning model to improve the solutions and predictions of wall quantities for coarser meshes. This strategy eliminates the usage of wall models and extends the range of applicability of grid sizes without a significant drop in accuracy of solutions. Random forest methodology coupled with a bagged aggregation algorithm helps in modeling a correction factor for the velocity gradient at the first grid points. The training data set for the ML model extracted from fine-grid RANS, includes neighbor cell information to address the memory effect of turbulence, and an optimal set of parameters to model the gradient correction factor. The successful demonstration of accurate predictions of wall-shear for coarse grids using this methodology, provides the confidence to build machine learning models to use DNS or high-fidelity modeling results as training data for reduced-order turbulence model development. This paves the way to integrate machine learning with RANS to produce accurate solutions with significantly lesser computational costs for hypersonic boundary layer problems.