Can Wang, Mitra Baratchi, Thomas Bäck, Holger Hoos, Steffen Limmer, Markus Olhofer,
"Towards time series feature engineering in automated machine learning for multi-step forecasting",
Proc. of International Conference on Time Series and Forecasting (ITISE2022), vol. 18, no. 1, 2022.
Abstract
Feature engineering is an essential step in the pipelines used for many machine learning tasks, including time-series forecasting. Although existing AutoML approaches partly automate feature engineering, they do not support specialised approaches for time-series data such as multi-step forecasting. Multi-step forecasting is the task of predicting a sequence of values in a time series. Two kinds of approaches are commonly used for multi-step forec...
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Nikolas Hohmann, Mariusz Bujny, Jürgen Adamy, Markus Olhofer,
"Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios",
IEEE Congress on Evolutionary Computation, 2022.
Abstract
In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the cur...
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Rodrigo Canaan, Xianbo Gao, Julian Togelius, Andy Nealen, Stefan Menzel,
"Generating and Adapting to Diverse Ad-Hoc Partners in Hanabi",
IEEE Transactions on Games, 2022.
Abstract
Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect. In this paper, we focus on ad-hoc settings with no previous coordination between partners. We introduce a “Bayesian Meta-Agent” that maintains a belief distribution over hypotheses of partner policies. The policies that serve as initial hypotheses...
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Thomas Schmitt, Matthias Hoffmann, Tobias Rodemann, Jürgen Adamy,
"Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control",
Inventions 2022, vol. 7, no. 3, 2022.
Abstract
We present a new two-step approach for automatized a posteriori decision making in multi-objective
optimization problems. In the first step, a knee region is determined based on the normalized Euclidean
distance from a hyperplane defined by the furthest Pareto solution and the negative unit vector. The size of
the knee region depends on the Pareto front’s shape and a design parameter. In the second step, preferences
for all objectives formu...
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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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Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann,
"Coordinated Adaptation of Reference Vectors and Scalarizing Functions in Evolutionary Many-objective Optimization",
IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022.
Abstract
It is highly desirable to adapt the reference vectors to unknown Pareto fronts in decomposition based evolutionary many-objective optimization. While adapting the reference vectors enhances the diversity of the achieved solutions, it often decelerates the convergence performance. To address this dilemma, we propose to adapt the reference vectors and the scalarizing functions in a coordinated way. On the one hand, the adaptation of the reference v...
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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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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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