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KI 2022: Advances in Artificial Intelligence

45th German Conference on AI, Trier, Germany, September 19–23, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Ralph Bergmann

    1. University of Trier, Trier, Germany

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  2. Lukas Malburg

    1. University of Trier, Trier, Germany

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  3. Stephanie C. Rodermund

    1. University of Trier, Trier, Germany

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  4. Ingo J. Timm

    1. University of Trier, Trier, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13404)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: KI 2022.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xxii
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  2. An Implementation of Nonmonotonic Reasoning with System W

    • Christoph Beierle, Jonas Haldimann, Daniel Kollar, Kai Sauerwald, Leon Schwarzer
    Pages 1-8

  3. Leveraging Implicit Gaze-Based User Feedback for Interactive Machine Learning

    • Omair Bhatti, Michael Barz, Daniel Sonntag
    Pages 9-16

  4. The Randomness of Input Data Spaces is an A Priori Predictor for Generalization

    • Martin Briesch, Dominik Sobania, Franz Rothlauf
    Pages 17-30

  5. Communicating Safety of Planned Paths via Optimally-Simple Explanations

    • Noel Brindise, Cedric Langbort
    Pages 31-44

  6. Assessing the Performance Gain on Retail Article Categorization at the Expense of Explainability and Resource Efficiency

    • Eduardo Brito, Vishwani Gupta, Eric Hahn, Sven Giesselbach
    Pages 45-52

  7. Enabling Supervised Machine Learning Through Data Pooling: A Case Study with Small and Medium-Sized Enterprises in the Service Industry

    • Leonhard Czarnetzki, Fabian Kainz, Fabian Lächler, Catherine Laflamme, Daniel Bachlechner
    Pages 53-59

  8. Unsupervised Alignment of Distributional Word Embeddings

    • Aïssatou Diallo, Johannes Fürnkranz
    Pages 60-74

  9. NeuralPDE: Modelling Dynamical Systems from Data

    • Andrzej Dulny, Andreas Hotho, Anna Krause
    Pages 75-89

  10. Deep Neural Networks for Geometric Shape Deformation

    • Aida Farahani, Julien Vitay, Fred H. Hamker
    Pages 90-95

  11. Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling

    • Jan David Hüwel, Florian Haselbeck, Dominik G. Grimm, Christian Beecks
    Pages 96-114Open Access

  12. Optimal Fixed-Premise Repairs of \(\mathcal{E}\mathcal{L}\) TBoxes

    • Francesco Kriegel
    Pages 115-130

  13. Health and Habit: An Agent-based Approach

    • Veronika Kurchyna, Stephanie Rodermund, Jan Ole Berndt, Heike Spaderna, Ingo J. Timm
    Pages 131-145

  14. Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations

    • Mena Leemhuis, Özgür L. Özçep, Diedrich Wolter
    Pages 146-159

  15. Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning

    • Christian Löwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon, Jonas K. Falkner, Lars Schmidt-Thieme
    Pages 160-172

  16. HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant

    • Nils Neumann, Sven Wachsmuth
    Pages 173-186

  17. Automated Kantian Ethics: A Faithful Implementation

    • Lavanya Singh
    Pages 187-208

  18. PEBAM: A Profile-Based Evaluation Method for Bias Assessment on Mixed Datasets

    • Mieke Wilms, Giovanni Sileno, Hinda Haned
    Pages 209-223

  19. Back Matter

    Pages 225-225
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Other volumes

  1. KI 2022: Advances in Artificial Intelligence

Keywords

About this book

This book constitutes the refereed proceedings of the 45th German Conference on Artificial Intelligence, KI 2022, held in September 2022.

The 12 full and 5 short papers were carefully reviewed and selected from 51 submissions. Additionally, five abstracts of invited talks are included. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research.

Due to COVID-19 the conference was held virtually.

The chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Editors and Affiliations

  • University of Trier, Trier, Germany

    Ralph Bergmann, Lukas Malburg, Stephanie C. Rodermund, Ingo J. Timm

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