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Charlie Street, Bruno Lacerda, Michal Staniaszek, Manuel Mühlig, Nick Hawes, "A Context-Aware Simulator for Rapidly Evaluating Multi-Robot Systems ", The 3rd International Symposium on Multi-Robot and Multi-Agent Systems, 2021.

Abstract

Evaluation methods analyse multi-robot behaviour under a given property. Physics-based simulators and model checking are common evaluation tools. Physics-based simulators are more representative of real robot behaviour, but are often slowed by computational demand. Model checking techniques are faster, but use simplified environment models. In this paper, we combine these method's strengths to build an abstract simulator for rapidly evaluating co...



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Martin Stadie, Tobias Rodemann, Andre Burger, Florian Jomrich, Steffen Limmer, Sven Rebhan, Hibiki Saeki, "V2B Vehicle to Building Charging Manager", EVTeC: 5th International Electric Vehicle Technology Conference 2021, 2021.

Abstract

Due to a fast rise in the share of renewable energy with a corresponding destabilizing impact on the energy grid and a rapidly growing share of electric vehicles (EVs), the smart integration of electric mobility and facility energy management promises substantial social, ecologic, and economical benefits for drivers, facility and grid operators, and society in general. Vehicle-to-grid (V2G) technologies, which allow a bi-directional energy flow t...



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Samuele Vinanzi, "Developmental Collaborative Intelligence for Embodied Agents ", University of Manchester, 2021.

Abstract

Robots stand at the heart of a techno-scientific revolution which promises to alter the way in which we conceive our society. Recent discoveries point towards a future in which artificial agents will become fully integrated in our social structures, thus becoming important actors in our life. In this scenario, it will be critical for them to be able to understand us in the most human-like fashion and to assist us in our routines. We state that c...



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Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao, "Improving Evolutionary Optimization through Prediction of Inductive Biases with Applications to Shape Optimization", IEEE Symposium Series on Computational Intelligence, 2021.

Abstract

Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying ...



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Zhenpeng Shi, Kalman Graffi, David Starobinski, Nikolay Matyunin, "Threat Modeling Tools: A Taxonomy", IEEE Security and Privacy , no. 1, pp. 2-13, 2021.

Abstract

Threat modeling tools allow to identify weaknesses in a system design. Yet, understanding conceptual differences between the tools is not trivial. We propose a taxonomy of threat modeling tools, and use it to compare several popular ones. Alongside, we illustrate differences between the tools with a usage example....



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Sandra Ittner, Dominik Mühlbacher, Alexandra Neukum, Thomas Weisswange, "User evaluation of passenger assistance system concepts on public highways", Frontiers in Psychology, vol. 12, pp. 725808, 2021.

Abstract

There is ample research on assistance systems for drivers in conventional and automated vehicles. In the past, those systems were primarily developed to increase safety. Since many common risks have by now been mitigated through such systems, the research and development focus was extended to also include comfort-related assistance systems. However, in this work, the passenger has rarely been taken into account explicitly, although it has been sh...



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Patricia Wollstadt and Sebastian Schmitt, "Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory", 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.

Abstract

An important issue during an engineering design process is to develop an understanding which design parameters have the most influence on the performance. Especially in the context of optimization approaches this knowledge is crucial in order to realize an efficient design process and achieve high-performing results. Information theory provides powerful tools to investigate these relationships because measures are model-free and thus also capture...



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Stefan Fuchs and Anna Belardinelli, "Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks", Frontiers in Neurorobotics, vol. 15, pp. 33, 2021.

Abstract

Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and ...



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Xiaofen Lu, Ke Tang, Stefan Menzel, Xin Yao, "Dynamic Optimization in Fast-Changing Environments via Offline Evolutionary Search", IEEE Transactions on Evolutionary Computation, 2021.

Abstract

Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world problems. For its robustness with respect to noise, Evolutionary Algorithms (EAs) have been expected to have great potential for dynamic optimization. Many dynamic optimization methods such as diversity-driven methods, memory methods, and prediction methods have been propo...



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Thomas Uriot, Dario Izzo, Luís Simões, Rasit Abay, Nils Einecke, Sven Rebhan, Jose Martinez-Heras, Francesca Letizia, Jan Siminski, Klaus Merz, "Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition", Astrodynamics, 2021.

Abstract

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to s...



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