Kontextadaptive Dienstnutzung in Ubiquitous Computing Umgebungen

Die vorliegende Arbeit präsentiert einen Ansatz zur Spezifikation und Implementierung von kontextadaptiven Anwendungen in einer Ubiquitous Computing Umgebung. Grundlegend ist dabei das Konzept der kontextadaptiven Dienstnutzung, die sowohl die kontextadaptive Selektion als auch Ausführung von Diensten umfasst. Die kontextadaptive Selektion erweitert grundlegende Techniken der Dienstvermittlung insofern, dass ein Matching nicht ausschließlich durch die Spezifikation von gewünschten Dienstattributen erfolgt, sondern auch Kontextinformationen Berücksichtigung finden. Die Ausführung eines Dienstes kann ebenfalls an kontextuelle Bedingungen geknüpft werden. Eine realisierte Kombination von kontextadaptiver Selektion und Ausführung ermöglicht eine sowohl personalisierte als auch situationsbezogene Bereitstellung von Diensten. Kern der kontextadaptiven Dienstnutzung ist dabei ein Datenzentrisches Protokoll, welches die Weiterleitung (Routing) von Anwendungsdaten anhand kontextueller Einschränkungen erlaubt. Dieser Ansatz gestattet neben der kontextadaptiven Nutzung individueller Dienste auch die spontane Komposition von Diensten in einer Ubiquitous Computing Umgebung. Ferner wird ein Konzept zur dynamischen Rollenverwaltung für Endgeräte in einer Ubiquitous Computing Umgebung entwickelt und ein Verfahren zur Konstruktion von Kontextinformationen innerhalb eines Ad-hoc-Sensornetzwerks vorgestellt

Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection

As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and well-researched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks. In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfolio-based planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 201

Smart CAPs for Smart Its - Context Detection for Mobile Users

Context detection for mobile users plays a major role for enabling novel, human-centric interfaces. For this, we introduce a context detection scheme for disseminated, computer empowered sensors, referred to as Smart-Its [7]. Context-detection takes place without requiring any central point of control, and supports push as well as pull modes. Our solution is based on an in-network composition approach relying on so-called smart context-aware packets (sCAPs). sCAPs travel thru a sensor network governed by an enclosed retrieving plan, specifying which sensors to visit for gaining a specific piece of context information. For enhanced flexibility, the retrieving plan itself may be dynamically altered in accordance to past sensor readings

Delfi: Online Planner Selection for Cost-Optimal Planning (planner abstract)

Cost-optimal planning has not seen many successful approaches that work well across all domains. Some costoptimal planners excel on some domains, while exhibiting less exciting performance on others. For a particular domain, however, there is often a cost-optimal planner that works extremely well. For that reason, portfolio-based techniques have recently become popular. These either decide offline on a particular resource allocation scheme for a given collection of planners or try to perform an online classification of a given planning task to select a planner to be applied to solving the task at hand. Our planner Delfi is an online portfolio planner. In contrast to existing techniques, Delfi exploits deep learning techniques to learn a model that predicts which of the planners in the portfolio can solve a given planning task within the imposed time and memory bounds. Delfi uses graphical representations of a planning task which allows exploiting existing tools for image convolution. In this planner abstract, we describe the techniques used to create our portfolio planner

Logic-based ontology comparison and module extraction, with an application to DL-Lite

We develop a formal framework for comparing different versions of ontologies, and apply it to ontologies formulated in terms of DL-Lite, a family of ‘lightweight’ description logics designed for data-intensive applications. The main feature of our approach is that we take into account the vocabulary (=signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between ontologies are introduced and their respective applications for ontology development and maintenance discussed. These variants are obtained by generalising the notion of conservative extension from mathematical logic and by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, by taking into account additional context ontologies, and by considering a model-theoretic, language-independent notion of difference. We compare these variants, study their meta-properties, determine the computational complexity of the corresponding reasoning tasks, and present decision algorithms. Moreover, we show that checking inseparability can be automated by means of encoding into QBF satisfiability and using off-the-shelf general purpose QBF solvers. Inseparability relations between ontologies are then used to develop a formal framework for (minimal) module extraction. We demonstrate that different types of minimal modules induced by these inseparability relations can be automatically extracted from real-world medium-size DL-Lite ontologies by composing the known tractable syntactic locality-based module extraction algorithm with our non-tractable extraction algorithms and using the multi-engine QBF solver aqme. Finally, we explore the relationship between uniform interpolation (or forgetting) and inseparability