Stephen Friess, Peter Tino, Stefan Menzel, Zhao Xu, Bernhard Sendhoff, Xin Yao,
"Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction",
International Joint Conference on Neural Networks, 2022.
Abstract
Traditional methods for solving problems within computer science rely mostly upon the application of handcrafted algorithms. As however manual engineering of them can be considered to be a tedious process, it is interesting to consider how far internal mechanisms can be directly learned in an end-to-end manner instead. This is especially tempting when considering metaheuristic and evolutionary optimization routines which rely inherently upon stoc...
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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao,
"Benchmarking Dynamic Capacitated Arc Routing Algorithms Using Real-World Traffic Simulation",
IEEE Congress on Evolutionary Computation, 2022.
Abstract
The combinatorial optimization of the dynamic capacitated arc routing problem (DCARP) targets to re-schedule the service plans of agents, such as vehicles in a city scenario, when dynamic events deteriorate the quality of the current schedule. Various algorithms have been proposed to solve DCARP instances in different dynamic scenarios. However, most existing work in literature developed algorithms and evaluated their performance based on artifi ...
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Jonathan Jakob, Martina Hasenjäger, Barbara Hammer,
"Reject Options for Incremental Regression Scenarios",
International Conference on Artificial Neural Networks (ICANN) 2022, 2022.
Abstract
Machine Learning with a Reject Option is the empowerment of an algorithm to abstain from prediction when the outcome is likely to be inaccurate. Although, already studied many decades ago, this field of machine learning has recently gained some traction again. However, most reject option applications concern themselves with classification tasks and from the little work that is available for regression systems all are about rejections in an offlin...
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Jonathan Jakob, André Artelt, Martina Hasenjäger, Barbara Hammer,
"SAMknn Regressor for Online Learning in Water Distribution Networks",
International Conference on Artificial Neural Networks (ICANN) 2022, 2022.
Abstract
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to the houses, buildings and industrial plants where it is consumed. In the flow of these networks anomalies can manifest themselves e.g. through leakages and or other unforeseen behaviour like fire runs. Since, each anomaly has the potential of being a leakage problem whe...
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Jiawen Kong, Wojtek Kowalczyk, Kees Jonkers, Stefan Menzel, Thomas Bäck,
"Improved Sample Type Identification for Multi-Class Imbalanced Classification with Real-World Applications",
18th International Conference on Data Science (ICDATA22), 2022.
Abstract
Driven by studying the nature of imbalanced data, researchers proposed to consider different types of samples (safe, borderline, rare samples and outliers) in the minority class. The idea was first proposed and evaluated on binary imbalanced classification problems and then extended to multi-class scenarios. However, simply extending the identification rule in binary scenarios to multi-class scenarios results in several problems, for example, a h...
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Charlie Street,
"Multi-Robot Coordination Under Temporal Uncertainty",
University of Oxford, University of Oxford, 2022.
Abstract
Sources of temporal uncertainty affect the duration and start time of robot actions
during execution. For example, mobile robots may slip on uneven terrain, slowing
them down. The presence of multiple robots in the environment contributes towards
temporal uncertainty, as robot interactions such as congestion affect navigation
performance. Existing multi-robot coordination solutions often disregard temporal
uncertainty through simplifying ass...
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Chao Wang,
"Design for Collaborative Intelligence: from Connected Vehicle, Autonomous Driving to Robotics.
",
Jiangnan University (Wuxi, China) online Forum, 2022.
Abstract
The aim of the intelligent system should be enhancing human’s ability instead of replacing them. There is a much larger space for humans and AI to complement each other than compete, because their advantages are located in different aspects. This complementarity can be called Collaborative Intelligence(CI), which enables machines to achieve goals in complex environments together with human. CI requires mutual understanding and seamless communic...
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Anna Belardinelli, Dirk Ruiken, Daniel Tanneberg,
"Intention estimation from gaze and motion features for human-robot
shared-control object manipulation",
IROS 2022, 2022.
Abstract
Shared control can help in teleoperated object
manipulation by assisting with the execution of the user’s
intention. To this end, robust and prompt intention estimation
is needed, which relies on behavioral observations. Here, an
intention estimation framework is presented, which uses natural
gaze and motion features to predict the current action and
the target object. The system is trained and tested in a
simulated environment with pic...
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Viktor Losing and Martina Hasenjäger,
"A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment",
Scientific Data, vol. 9, no. 473, 2022.
Abstract
Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, the database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU−, FSR−, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 ± 7.64 cm, 72.9 ± 8.7 kg) wore a full-body Lycra suit...
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Lukas Hindemith, Jan Goepfert, Christiane Wiebel, Britta Wrede, Anna-Lisa Vollmer,
"Why robots should be technical: Correcting mental models through technical architecture concepts",
Interaction Studies, 2022.
Abstract
Research in social robotics is commonly focused on designing robots that
imitate human behavior. While this might increase a user’s satisfaction and
acceptance of robots at fir st glance, it does not automatically aid a non-
expert user in naturally interacting with robots, and might hurt their ability
to correctly anticipate a robot’s capabilities. We argue that a faulty mental
model, that the user has of the robot, is one of the main s...
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