Uwe-Philipp Käppeler

Efficiently Managing Context Information for Large-Scale Scenarios

In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify major classes of context data and detail on their characteristics relevant for efficiently managing large amounts of it. Based on that, we argue that for large-scale systems it is beneficial to have special-purpose servers that are optimized for managing a certain class of context data. In the Nexus project we have implemented five servers for different classes of context data and a very flexible federation middleware integrating all these servers. For each of them, we highlight in which way the requirements of the targeted class of data are tackled and discuss our experiences

Benefits of Integrating Meta Data into a Context Model

Meta data - data about data - improves the value of the operational data by giving applications and users additional information on the datas origin, its precision or staleness. We outline the benefits of modeling meta data in context models: it can be used for resource finding, enhanced data selection, trust and data quality issues and sensor fusion. We show how meta data included into an object-based context model influences the data modeling and the selection process in the query language. Finally, we describe our implementation of the presented functionality in the Nexus platform

Evaluating Coupled Selection Equations for Dynamic Task Assignment Using a Behavior Framework

In this paper we focus on methods for a reliable and robust mechanism to distribute roles among a team of cooperating robots. In previous work, we showed the principal applicability of a novel approach based on self organization using coupled selection equations. To show the applicability in the robocup scenario we used a simple scenario to assign the roles attacker and defender. In this paper we present the application of the novel approach to more realistic and complex scenarios like kick-off or pass play. One of the critical parts in this method is the parameterization of utility and activation functions used to determine the additional parameters.

Supervised learning algorithm for automatic adaption of situation templates using uncertain data

In this paper a learning algorithm for the automatic adaption of a situation template is presented. The approach strongly relies on human-machine interaction as user feedback is a substantial part to automatically adapt a global knowledgebase in this case. The work bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware user-friendly system for sharing context data.

Extended TA Algorithm for Adapting a Situation Ontology

In this work we introduce an improved version of a learning algorithm for the automatic adaption of a situation ontology (TAA) [1] which extends the basic principle of the learning algorithm. The approach bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware system for sharing context data.

Event based distributed real-time communication architecture for multi-agent systems

In this paper an event-based distributed real-time communication architecture for multi-agent systems is presented. The architecture was designed regarding the needs of hardware-based multi-agent systems, especially robotic systems. This includes in particular fast and reliable transportation of messages in unstable wireless networks. Furthermore the architecture is usable for different agent-based software systems and allows communication between different systems even if they have a complete different representation of internal data. The architecture was evaluated in the RoboCup scenario with a multi-agent system of 5 robots interacting in a highly dynamic environment. Using this system, the RoboCup team of the University of Stuttgart won the RoboCup German Open Championship and World Championship in 2009.

Advanced Data Logging in RoboCup

In this work an advanced data logging approach for the RoboCup domain using an autonomous camera man is presented. It includes approaches from the field of real-time robot message logging, situation based camera control and 3D video visualisation. Based on the implementation of this work the RoboCup team of the IPVS of the University of Stuttgart won the first price in the scientific challenge of the RoboCup World Championship 2009 in Graz.

Physical Simulation of the Dynamical Behavior of Three-Wheeled Omni-directional Robots

Hardware simulation is a very efficient way for parameter tuning. We developed a Simulink-based simulator for the navigation components of our robotic soccer team. This physical simulation has interfaces to be interconnected with the higher levels of the real control software and is therefore able to perform an overall simulation of single robots.