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This book constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Smart Card Research and Advanced Applications, CARDIS 2016, held in Cannes, France, in November 2016. The 15 revised full papers presented in this book were carefully reviewed and selected from 29 submissions. The focus of the conference was on all aspects of the design, development, deployment, validation, and application of smart cards or smart personal devices.
Unternehmen stehen oft vor der Frage, nach welchen Kriterien Investitionsentscheidungen in anderen Ländern und an ausländischen Standorten – ggf. im Wettbewerb zu anderen Geografien – zu treffen sind. Aufgrund der schwierigen Revidierbarkeit von Standortentscheidungen ist zu erwarten, dass die Unternehmen eine Vielzahl von Kriterien in ihre Überlegungen einbeziehen und die in Betracht kommenden Standorte diesbezüglich komparativ prüfen.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
Embodied artificial agents operating in dynamic, real-world environments need architectures that support the special requirements that exist for them. Architectures are not always designed from scratch and the system then implemented all at once, but rather, a step-wise integration of components is often made to increase functionality. Our work aims to increase flexibility and robustness by integrating a task planner into an existing architecture and coupling the planning process with the preexisting execution and the basic monitoring processes. This involved the conversion of monolithic SMACH scenario scripts (state-machine execution scripts) into modular states that can be called dynamically based on the plan that was generated by the planning process. The procedural knowledge encoded in such state machines was used to model the planning domain for two RoboCup@Home scenarios on a Care-O-Bot 3 robot [GRH+08]. This was done for the JSHOP2 [IN03] hierarchical task network (HTN) planner. A component which iterates through a generated plan and calls the appropriate SMACH states [Fie11] was implemented, thus enabling the scenarios. Crucially, individual monitoring actions which enable the robot to monitor the execution of the actions were designed and included, thus providing additional robustness.
RPSL meets lightning: A model-based approach to design space exploration of robot perception systems
(2017)
Mit dem Projekt Pro-MINT-us hat sich die Hochschule Bonn-Rhein-Sieg erfolgreich im „Qualitätspakt Lehre“ beworben. Im Fokus steht dabei eine bessere Begleitung der Studierenden im Übergang von der Schule zur Hochschule. Mit Hilfe der Projektmittel konnten u.a. zwei Stellen geschaffen werden, die die Studierenden im Bereich „wissenschaftliches Schreiben“ unterstützen sollen.