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Ontologien spielen eine zentrale Rolle für die formalisierte Repräsentation von Wissen und Informationen sowie für die Infrastruktur des sogenannten semantic web. Trotz früherer Initiativen der Bibliotheken und Gedächtnisinstitutionen hat sich die deutschsprachige Musikwissenschaft insgesamt nur sehr zögerlich dem Thema genähert. Im Rahmen einer Bestandsaufnahme werden neben der Erläuterung grundlegender Konzepte, Herausforderungen und Herangehensweisen bei der Modellierung von Ontologien daher auch vielversprechende Modelle und bereits erprobte Anwendungsbeispiele für eine ‚semantische‘ digitale Musikwissenschaft identifiziert. / Ontologies play a crucial role for the formalised representation of knowledge and information as well as for the infrastructure of the semantic web. Despite early initiatives that were driven by libraries and memory institutions, German musicology as a whole has turned very slowly to the subject. In an overview the author addresses basic concepts, challenges, and approaches for ontology design and identifies models and use cases with promising applications for a ‚semantic‘ digital musicology.

Ce travail de thèse se situe dans le cadre du projet Terra Dynamica visant à peupler une ville virtuelle avec des agents qui simulent des piétons et des véhicules. L’objectif de notre travail est de rendre l’environnement compréhensible par les agents de la simulation afin qu’ils puissent exhiber des comportements crédibles. Les premiers travaux qui ont été proposés pour la modélisation sémantique des environnements virtuels gardent toujours un lien de dépendance avec la représentation graphique pré-existante de l’environnement. Cependant, l’information sémantique représentée dans ce genre d’approches est difficilement exploitable par les agents pour effectuer des procédures de raisonnement complexes en dehors des algorithmes de navigation. Nous présentons dans cette thèse un modèle de représentation de la sémantique de l’environnement qui fournit aux agents des données sur l’utilisation des objets de l’environnement pour permettre au mécanisme d’aide à la décision de produire des comportements crédibles. Par ailleurs, en réponse à des contraintes inhérentes à la simulation urbaine, notre approche est capable de traiter un grand nombre d’agents, en temps réel. Notre modèle est basé sur le principe que les objets de l’environnement proposent des services permettant de réaliser les actions avec différentes qualités. Nous avons donc représenté les informations sémantiques des objets liées à leur utilisation sous forme de services dans une ontologie de services. Nous avons utilisé cette ontologie de services pour calculer une qualité de service QoS qui nous permet de trier les différents objets permettant de réaliser une même action. Ainsi, nous pouvons comparer entre les services proposés par les objets pour proposer aux agents les meilleurs objets leur permettant de réaliser leurs actions afin d’acquérir une crédibilité comportementale. Afin d’évaluer l’impact de notre modèle sur la crédibilité des comportements produits, nous avons défini un protocole d’évaluation dédié aux modèles de représentation de la sémantique dans les environnements. Dans ce protocole, des observateurs doivent évaluer le caractère crédible des comportements produits par le simulateur à partir d’un modèle sémantique de l’environnement. Grâce à cette évaluation, nous montrons que notre modèle permet de simuler des agents dont le comportement est jugé comme crédible par des observateurs humains. Nous présentons également une évaluation qualitative de la capacité de notre modèle de passer à l’échelle et de répondre aux contraintes d’une simulation temps-réel. Cette évaluation, nous a permis de montrer que les caractéristiques de l’architecture de notre modèle nous permettent de répondre en un temps raisonnable aux demandes d’un grand nombre d’agents. / This work is part of the Terra Dynamica project whose objective was to populate a virtual city with agents that simulate pedestrians and vehicles. The aim of our work is to make agents which understand their environment so they can produce credible behaviors The first proposed solutions for the semantic modeling of virtual environments still keep a link with the pre-existing graphic representation of the environment. However, the semantic information represented in this kind of approach is difficult to use by the agents to perform complex reasoning procedures outside the navigation algorithms. In this thesis we present a semantic representation model of the environment that provides the agents with data on the use of environmental objects in order to allow the decision mechanism to produce credible behaviors. Furthermore, in response to the constraints that are inherent to the urban simulation, our approach is capable of handling a large number of agents in real time. Our model is based on the principle that environmental objects provide services for performing actions with different qualities. We have therefore represented the semantic information of the objects related to their use, as services in an ontology of services. We used this ontology of services to calculate a QoS which allows us to sort the different objects which all perform the same action. Thus, we can compare between the services offered by different objects in order to provide the agents with the best objects that allow them to carry out their actions and exhibit behavioral credibility. To assess the impact of our model on the credibility of the produced behaviors, we defined an evaluation protocol for the semantic representation of virtual environment models. In this protocol, observers must assess the credibility of behaviors produced by the simulator using a semantic model of the environment. Through this evaluation, we show that our model can simulate agents whose behavior is deemed credible by human observers. We also present a qualitative assessment of the ability of our model to scale and meet the constraints of a real-time simulation. This evaluation allowed us to show that the characteristics of the architecture of our model allow us to respond in a reasonable amount of time to requests from a large number of agents.

La représentation des connaissances et le raisonnement sont au cœur du grand défi de l'Intelligence Artificielle. Plus précisément, dans le contexte des applications robotiques, la représentation des connaissances et les approches de raisonnement sont nécessaires pour résoudre les problèmes de décision auxquels sont confrontés les robots autonomes lorsqu'ils évoluent dans des environnements incertains, dynamiques et complexes ou pour assurer une interaction naturelle dans l'environnement humain. Dans un système d'interaction robotique, l'information doit être représentée et traitée à différents niveaux d'abstraction: du capteur aux actions et plans. Ainsi, la représentation des connaissances fournit les moyens de décrire l'environnement avec différents niveaux d'abstraction qui permettent d'effectuer des décisions appropriées. Dans cette thèse, nous proposons une méthodologie pour résoudre le problème de l'interaction multimodale en décrivant une architecture d'interaction sémantique basée sur un cadre qui démontre une approche de représentation et de raisonnement avec le langage (EKRL environment knowledge representation language), afin d'améliorer l'interaction entre les robots et leur environnement. Ce cadre est utilisé pour gérer le processus d'interaction en représentant les connaissances impliquées dans l'interaction avec EKRL et en raisonnant pour faire une inférence. Le processus d'interaction comprend la fusion des valeurs des différents capteurs pour interpréter et comprendre ce qui se passe dans l'environnement, et la fission qui suggère un ensemble détaillé d'actions qui sont mises en œuvre. Avant que ces actions ne soient mises en œuvre par les actionneurs, ces actions sont d'abord évaluées dans un environnement virtuel qui reproduit l'environnement réel pour évaluer la faisabilité de la mise en œuvre de l'action dans le monde réel. Au cours de ces processus, des capacités de raisonnement sont nécessaires pour garantir une exécution globale d'un scénario d'interaction. Ainsi, nous avons fourni un ensemble de techniques de raisonnement pour effectuer de l’inférence déterministe grâce à des algorithmes d'unification et des inférences probabilistes pour gérer des connaissances incertaines en combinant des modèles relationnels statistiques à l'aide des réseaux logiques de Markov (MLN) avec EKRL. Le travail proposé est validé à travers des scénarios qui démontrent l’applicabilité et la performance de notre travail dans les applications du monde réel. / Knowledge Representation and Reasoning is at the heart of the great challenge of Artificial Intelligence. More specifically, in the context of robotic applications, knowledge representation and reasoning approaches are necessary to solve decision problems that autonomous robots face when it comes to evolve in uncertain, dynamic and complex environments or to ensure a natural interaction in human environment. In a robotic interaction system, information has to be represented and processed at various levels of abstraction: From sensor up to actions and plans. Thus, knowledge representation provides the means to describe the environment with different abstraction levels which allow performing appropriate decisions. In this thesis we propose a methodology to solve the problem of multimodal interaction by describing a semantic interaction architecture based on a framework that demonstrates an approach for representing and reasoning with environment knowledge representation language (EKRL), to enhance interaction between robots and their environment. This framework is used to manage the interaction process by representing the knowledge involved in the interaction with EKRL and reasoning on it to make inference. The interaction process includes fusion of values from different sensors to interpret and understand what is happening in the environment, and the fission which suggests a detailed set of actions that are for implementation. Before such actions are implemented by actuators, these actions are first evaluated in a virtual environment which mimics the real-world environment to assess the feasibility of the action implementation in the real world. During these processes, reasoning abilities are necessary to guarantee a global execution of a given interaction scenario. Thus, we provided EKRL framework with reasoning techniques to draw deterministic inferences thanks to unification algorithms and probabilistic inferences to manage uncertain knowledge by combining statistical relational models using Markov logic Networks(MLN) framework with EKRL. The proposed work is validated through scenarios that demonstrate the usability and the performance of our framework in real world applications.

Description Logics (DLs) are a family of logic-based knowledge representation (KR) formalisms designed to represent and reason about static conceptual knowledge in a semantically well-understood way. On the other hand, standard action formalisms are KR formalisms based on classical logic designed to model and reason about dynamic systems. The largest part of the present work is dedicated to integrating DLs with action formalisms, with the main goal of obtaining decidable action formalisms with an expressiveness significantly beyond propositional. To this end, we offer DL-tailored solutions to the frame and ramification problem. One of the main technical results is that standard reasoning problems about actions (executability and projection), as well as the plan existence problem are decidable if one restricts the logic for describing action pre- and post-conditions and the state of the world to decidable Description Logics. A smaller part of the work is related to decidable extensions of Description Logics with concrete datatypes, most importantly with those allowing to refer to the notions of space and time.

<div>Stochastic programs are standard models for decision-making under uncertainty and have been extensively studied in the operations research literature. In general, stochastic programming involves minimizing an expected cost function, where the expectation is with respect to fully specified stochastic models that quantify the aleatoric or `inherent' uncertainty in the decision-making problem. In practice, however, the stochastic models are unknown but can be estimated from data, introducing an additional epistemic uncertainty into the decision-making problem. The Bayesian framework provides a coherent way to quantify the epistemic uncertainty through the posterior distribution by combining prior beliefs of the decision-makers with the observed data. Bayesian methods have been used for data-driven decision-making in various applications such as inventory management, portfolio design, machine learning, optimal scheduling, and staffing, etc.</div><div> </div><div>Bayesian methods are challenging to implement, mainly due to the fact that the posterior is computationally intractable, necessitating the computation of approximate posteriors. Broadly speaking, there are two methods in the literature implementing approximate posterior inference. First are sampling-based methods such as Markov Chain Monte Carlo. Sampling-based methods are theoretically well understood, but they suffer from various issues like high variance, poor scalability to high-dimensional problems, and have complex diagnostics. Consequently, we propose to use optimization-based methods collectively known as variational inference (VI) that use information projections to compute an approximation to the posterior. Empirical studies have shown that VI methods are computationally faster and easily scalable to higher-dimensional problems and large datasets. However, the theoretical guarantees of these methods are not well understood. Moreover, VI methods are empirically and theoretically less explored in the decision-theoretic setting.</div><div><br></div><div> In this thesis, we first propose a novel VI framework for risk-sensitive data-driven decision-making, which we call risk-sensitive variational Bayes (RSVB). In RSVB, we jointly compute a risk-sensitive approximation to the `true' posterior and the optimal decision by solving a minimax optimization problem. The RSVB framework includes the naive approach of first computing a VI approximation to the true posterior and then using it in place of the true posterior for decision-making. We show that the RSVB approximate posterior and the corresponding optimal value and decision rules are asymptotically consistent, and we also compute their rate of convergence. We illustrate our theoretical findings in both parametric as well as nonparametric setting with the help of three examples: the single and multi-product newsvendor model and Gaussian process classification. Second, we present the Bayesian joint chance-constrained stochastic program (BJCCP) for modeling decision-making problems with epistemically uncertain constraints. We discover that using VI methods for posterior approximation can ensure the convexity of the feasible set in (BJCCP) unlike any sampling-based methods and thus propose a VI approximation for (BJCCP). We also show that the optimal value computed using the VI approximation of (BJCCP) are statistically consistent. Moreover, we derive the rate of convergence of the optimal value and compute the rate at which a VI approximate solution of (BJCCP) is feasible under the true constraints. We demonstrate the utility of our approach on an optimal staffing problem for an M/M/c queue. Finally, this thesis also contributes to the growing literature in understanding statistical performance of VI methods. In particular, we establish the frequentist consistency of an approximate posterior computed using a well known VI method that computes an approximation to the posterior distribution by minimizing the Renyi divergence from the ‘true’ posterior.</div>

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Aus der Einleitung: „Terminological knowledge representation systems (TKR systems) are powerful tools not only to represent but also to reason about the knowledge on the terminology of an application domain. Their particular power lie in their ability to infer implicit knowledge from the knowledge explicitly stored in a knowledge base. Mainly, a TKR system consists of three parts: First, a terminological knowledge base which contains the explicit description of the concepts relevant for the application domain. Second, an assertional knowledge base which contains the description of concrete individuals and their relations. This description of concrete individuals is realized using the terminology fixed in the terminological knowledge base. Third, a TKR system comprises an inference engine which is able to infer implicit properties of the defined concepts and individuals such as subclass/superclass relations amongst concepts (subsumption), the classifcation of all defned concepts with respect to the subclass/superclass relation. This yields the class taxonomy. whether there exists an interpretation of the terminology where a given concept has at least one instance (satisfiability), to enumerate all individuals that are instances of a given concept (retrieval), given a concrete individual, to enumerate the most specific concepts of the terminology this individual is an instance of.”

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