Explainable and Transparent AI and Multi-Agent Systems: 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6–10, 2024, Revised Selected Papers | SpringerLink
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Explainable and Transparent AI and Multi-Agent Systems

6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6–10, 2024, Revised Selected Papers

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Davide Calvaresi

    1. University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

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  2. Amro Najjar

    1. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

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  3. Andrea Omicini

    1. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

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  4. Reyhan Aydogan

    1. Ozyegin University, Istanbul, Türkiye

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  5. Rachele Carli

    1. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

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  6. Giovanni Ciatto

    1. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

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  7. Joris Hulstijn

    1. University of Luxembourg, Esch-sur-Alzette, Luxembourg

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  8. Kary Främling

    1. Umeå University, Umeå, Sweden

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14847)

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

Included in the following conference series:

Conference proceedings info: EXTRAAMAS 2024.

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About this book

This volume constitutes the papers of several workshops which were held in conjunction with the 6th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2024, in Auckland, New Zealand, during May 6–10, 2024.

The 13 full papers presented in this book were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: User-centric XAI; XAI and Reinforcement Learning; Neuro-symbolic AI and Explainable Machine Learning; and XAI & Ethics.

Keywords

Table of contents (13 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. User-centric XAI

    1. Front Matter

      Pages 1-1
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    2. Effect of Agent Explanations Using Warm and Cold Language on User Adoption of Recommendations for Bandit Problems

      • Selim Karaoǧlu, Marina Katoh, Titash Majumdar, Ethan Beaird, Feyza Merve Hafızoǧlu, Sandip Sen
      Pages 3-20

    3. Evaluation of the User-Centric Explanation Strategies for Interactive Recommenders

      • Berk Buzcu, Emre Kuru, Davide Calvaresi, Reyhan Aydoğan
      Pages 21-38

    4. Can Interpretability Layouts Influence Human Perception of Offensive Sentences?

      • Thiago Freitas dos Santos, Nardine Osman, Marco Schorlemmer
      Pages 39-57

    5. A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study

      • Berk Buzcu, Yvan Pannatier, Reyhan Aydoğan, Michael Ignaz Schumacher, Jean-Paul Calbimonte, Davide Calvaresi
      Pages 58-78

  3. XAI and Reinforcement Learning

    1. Front Matter

      Pages 79-79
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    2. Learning Temporal Task Specifications From Demonstrations

      • Mattijs Baert, Sam Leroux, Pieter Simoens
      Pages 81-98

    3. Temporal Explanations of Deep Reinforcement Learning Agents

      • Mark Towers, Yali Du, Christopher Freeman, Tim Norman
      Pages 99-115

    4. An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-Side Uncertainties

      • Sumanta Dey, Praveen Verma, Pallab Dasgupta, Soumyajit Dey
      Pages 116-136

    5. Model-Agnostic Policy Explanations: Biased Sampling for Surrogate Models

      • Bryan Lavender, Sandip Sen
      Pages 137-151

  4. Neuro-symbolic AI and Explainable Machine Learning

    1. Front Matter

      Pages 153-153
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    2. Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models

      • Victor Contreras, Michael Schumacher, Davide Calvaresi
      Pages 155-183

    3. py_ciu_image: A Python Library for Explaining Image Classification with Contextual Importance and Utility

      • Kary Främling, Ioan-Vlad Apopei, Gustav Grund Pihlgren, Avleen Malhi
      Pages 184-188

    4. Towards Interactive and Social Explainable Artificial Intelligence for Digital History

      • Richard Albrecht, Joris Hulstijn, Igor Tchappi, Amro Najjar
      Pages 189-202

  6. Back Matter

    Pages 243-243
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Other volumes

  1. Explainable and Transparent AI and Multi-Agent Systems

Editors and Affiliations

  • University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  • Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  • Alma Mater Studiorum - Università di Bologna, Bologna, Italy

    Andrea Omicini, Rachele Carli, Giovanni Ciatto

  • Ozyegin University, Istanbul, Türkiye

    Reyhan Aydogan

  • University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Joris Hulstijn

  • Umeå University, Umeå, Sweden

    Kary Främling

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