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Bridging the Gap Between AI and Reality

First International Conference, AISoLA 2023, Crete, Greece, October 23–28, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Bernhard Steffen

    1. TU Dortmund University, Dortmund, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14380)

Included in the following conference series:

Conference proceedings info: AISoLA 2023.

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Table of contents (27 papers)

  1. Front Matter

    Pages i-xiii
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  2. Panels and Keynotes

    1. Front Matter

      Pages 2-10
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    2. Deep Neural Networks, Explanations, and Rationality

      • Edward A. Lee
      Pages 11-21Open Access

  3. Verification Meets Learning and Statistics

    1. Front Matter

      Pages 23-23
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    2. Welcome Remarks from AISoLA 2023/Track C2 Chairs

      • Nils Jansen, Bettina Könighofer, Jan Křetínský, Kim Larsen
      Pages 25-32

    3. Shielded Reinforcement Learning for Hybrid Systems

      • Asger Horn Brorholt, Peter Gjøl Jensen, Kim Guldstrand Larsen, Florian Lorber, Christian Schilling
      Pages 33-54

    4. What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems

      • Saddek Bensalem, Chih-Hong Cheng, Wei Huang, Xiaowei Huang, Changshun Wu, Xingyu Zhao
      Pages 55-76

    5. DeepAbstraction++: Enhancing Test Prioritization Performance via Combined Parameterized Boxes

      • Hamzah Al-Qadasi, Yliès Falcone, Saddek Bensalem
      Pages 77-93

    6. Shielded Learning for Resilience and Performance Based on Statistical Model Checking in Simulink

      • Julius Adelt, Sebastian Bruch, Paula Herber, Mathis Niehage, Anne Remke
      Pages 94-118

    7. Formal XAI via Syntax-Guided Synthesis

      • Katrine Bjørner, Samuel Judson, Filip Cano, Drew Goldman, Nick Shoemaker, Ruzica Piskac et al.
      Pages 119-137

    8. Differential Safety Testing of Deep RL Agents Enabled by Automata Learning

      • Martin Tappler, Bernhard K. Aichernig
      Pages 138-159

    9. gRoMA: A Tool for Measuring the Global Robustness of Deep Neural Networks

      • Natan Levy, Raz Yerushalmi, Guy Katz
      Pages 160-170

    10. Optimized Smart Sampling

      • Maxime Parmentier, Axel Legay, Firmin Chenoy
      Pages 171-187

    11. Towards a Formal Account on Negative Latency

      • Clemens Dubslaff, Jonas Schulz, Patrick Wienhöft, Christel Baier, Frank H. P. Fitzek, Stefan J. Kiebel et al.
      Pages 188-214

  4. Safety Verification of DNNs

    1. Front Matter

      Pages 215-215
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    2. Track C1: Safety Verification of Deep Neural Networks (DNNs)

      • Daniel Neider, Taylor T. Johnson
      Pages 217-224

    3. Formal Verification of a Neural Network Based Prognostics System for Aircraft Equipment

      • Dmitrii Kirov, Simone Fulvio Rollini, Luigi Di Guglielmo, Darren Cofer
      Pages 225-240

    4. The Inverse Problem for Neural Networks

      • Marcelo Forets, Christian Schilling
      Pages 241-255

    5. Continuous Engineering for Trustworthy Learning-Enabled Autonomous Systems

      • Saddek Bensalem, Panagiotis Katsaros, Dejan Ničković, Brian Hsuan-Cheng Liao, Ricardo Ruiz Nolasco, Mohamed Abd El Salam Ahmed et al.
      Pages 256-278Open Access

    6. Benchmarks: Semantic Segmentation Neural Network Verification and Objection Detection Neural Network Verification in Perceptions Tasks of Autonomous Driving

      • Yonggang Luo, Jinyan Ma, Sanchu Han, Lecheng Xie
      Pages 279-290
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  1. Bridging the Gap Between AI and Reality

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About this book

This book constitutes the proceedings of the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023. The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning. 



Editors and Affiliations

  • TU Dortmund University, Dortmund, Germany

    Bernhard Steffen

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