Thomas Jatschka,
"Computational Optimization Approaches for Distributing Service Points for Mobility Applications and Smart Charging of Electric Vehicles",
Technical University Vienna, 2022.
Abstract
For many business models in the mobility domain an optimal distribution of service points in a customer community is needed. Examples are charging stations of electric vehicles (EVs), bicycle sharing stations, battery swapping stations, or repair stations. Two main challenges are to get the necessary data about the community and environment in order to estimate user demands, local constraints of potential locations, and other properties and to id...
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Leonore Röseler, Ingo Scholtes, Bernhard Sendhoff, Aniko Hannak,
"Willing to revise? Confidence and Recommendation Adoption in AI-Assisted Image Recognition",
International Conference on Hybrid Human-Artificial Intelligence (HHAI 2022), 2022.
Abstract
Artificial intelligence (AI) is increasingly used to assist humans in various aspects of everyday life, including high-stakes decision-making. Nevertheless, the question how to design human-AI teams that optimally integrate the strengths of both parties, while mitigating their respective weaknesses, is still open. This work investigates how different mechanisms for the integration of AI-generated recommendations influence the performance of AI-as...
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Patricia Wollstadt and Matti Krüger,
"Quantifying cooperation between artificial agents using information theory",
HHAI2022: Augmenting Human Intellect, vol. 354, pp. 302 - 304, 2022.
Abstract
When designing interactive human-machine systems, it is often assumed that it is desirable for such systems to behave cooperatively towards a human operator, to improve trust, acceptance, and usability, but also to increase task efficiency. To design cooperative HMI systems, we have to be able to define and quantitatively describe cooperative interactions, for example, to control, optimize, or evaluate system behavior. Despite the increased inter...
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Nazia Attari, David Schlangen, Heiko Wersing, Sina Zarriess,
"Generating Coherent and Informative Descriptions for Groups of Visual Objects and Categories: A Simple Decoding Approach",
INLG 2022 Proceedings, 2022.
Abstract
State-of-the-art image captioning models
achieve very good performance in generating
descriptions for instances of visual categories
and reasoning about them, e.g. imposing dis-
tinctiveness of the description in the context
of distractors. In this work, we propose an
inference mechanism that extends an instance-
level captioning model to generate coherent and
informative descriptions for groups of visual
objects from the same or differe...
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Stephen Friess,
"Inductive Biases and Metaknowledge Representations for Search-based Optimization",
University of Birmingham, 2022.
Abstract
"What I do not understand, I can still create.", H. Sayama. The following work follows closely the aforementioned bonmot. Guided by questions such as: "How can evolutionary processes exhibit learning behavior and consolidate knowledge?", "What are cognitive models of problem-solving?" and "How can we harness these altogether as computational techniques?", we clarify within this work essentials required to implement them for metaheuristic search a...
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Xilu Wang,
"Bayesian Evolutionary Optimization for
Heterogeneously Expensive Multi-objective
Optimization",
University of Surrey, 2022.
Abstract
Various multi-objective optimization algorithms have been proposed with a common
assumption that the evaluation of each objective function takes the same period of
time. Little attention has been paid to more general and realistic optimization scenarios where different objectives are evaluated by different computer simulations or
physical experiments with different time complexities (latencies) and only a very limited number of function evalua...
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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao,
"What Makes The Dynamic Capacitated Arc Routing Problem Hard To Solve: Insights From Fitness Landscape Analysis",
The Genetic and Evolutionary Computation Conference, 2022.
Abstract
The Capacitated Arc Routing Problem (CARP) aims at assigning vehicles to serve tasks which are located at different arcs in a graph. However, the originally planned routes are easily affected by different dynamic events like newly added tasks. This gives rise to Dynamic CARP (DCARP) instances, which need to be efficiently optimized for new high-quality service plans in a short time. However, it is unknown which dynamic events make DCARP instances...
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Jan Goepfert, Heiko Wersing, Barbara Hammer, Lukas Hindemith,
"Intuitiveness in Active Teaching",
IEEE Transactions on Human-Machine Systems, vol. 52, no. 3, pp. 458 - 467, 2022.
Abstract
Machine learning is a double-edged sword: it gives rise to astonishing
results in automated systems, but at the cost of tremendously large data
requirements. This makes many successful algorithms from machine learn-
ing unsuitable for human-machine interaction, where the machine must
learn from a small number of training samples that can be provided by a
user within a reasonable time frame. Fortunately, the user can tailor the
training data...
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Ernest Hutapea, Nivesh Dommaraju, Mariusz Bujny, Fabian Duddeck,
"Clustering Topologically-Optimized Designs based on Structural Deformation",
Munich Symposium on Lightweight Design 2021, 2022.
Abstract
Topology optimization can be used to generate a large set of lightweight structural solutions either by changing the constraints or the weights for different objectives in multi-objective optimization. Engineers must analyze and review the designs to select solutions according to their preference towards objectives such as structural compliance and crash performance. However, the sheer number of solutions challenge the engineers' decision-making ...
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Duc Anh Nguyen, Anna Kononova, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck,
"An Efficient Contesting Procedure for AutoML Optimization",
IEEE Access, 2022.
Abstract
Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome ...
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