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Jonathan Jakob, André Artelt, Martina Hasenjäger, Barbara Hammer, "Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams", Applied Artificial Intelligence, vol. 37, no. 1, 2023.

Abstract

In many real world scenarios, data is provided as a potentially infinite stream of samples, that are subject to changes in the underlying data distribution, a phenomenon often referred to as concept drift. A specific facet of concept drift is feature drift, where the relevance of a feature to the problem at hand changes over time. High-dimensionality of the data poses an additional challenge to learning algorithms operating in such environments....



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Steffen Limmer, Johannes Varga, Guenther Raidl, "An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicles", Applications of Evolutionary Computation 2023, Springer Nature Switzerland, pp. 3-18, 2023.

Abstract

In the present paper, we investigate the management of a fleet of electric vehicles. We propose a hybrid evolutionary approach for solving the problem of simultaneously planning the charging of electric vehicles and the assignment of electric vehicles to a set of reservations. The reservation assignment is optimized with an evolutionary algorithm while linear programming is used to compute optimal charging schedules. The evolutionary algorithm us...



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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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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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