Explainable and Transparent AI and Multi-Agent Systems

6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6¿10, 2024, Revised Selected Papers
Herausgegeben:Calvaresi, Davide; Najjar, Amro; Omicini, Andrea; Aydogan, Reyhan; Carli, Rachele; Ciatto, Giovanni; Hulstijn, Joris; Främling, Kary

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 14847
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-70073-6
• 2024
• Seitenzahl: 256
• Erscheinungstermin: 25. September 2024
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 394g
• ISBN-13: 9783031700736
• ISBN-10: 3031700732
• Artikelnr.: 71345028

Inhaltsangabe

.- User-centric XAI.
.- Effect of Agent Explanations Using Warm and Cold Language on User Adoption of Recommendations for Bandit Problem.
.- Evaluation of the User-centric Explanation Strategies for Interactive Recommenders.
.- Can Interpretability Layouts Influence Human Perception of Offensive Sentences?.
.- A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study.
.- XAI and Reinforcement Learning.
.- Learning Temporal Task Specifications From Demonstrations.
.- Temporal Explanations for Deep Reinforcement Learning Agents.
.- An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-side Uncertainties.
.- Model-Agnostic Policy Explanations: Biased Sampling for Surrogate Models.
.- Neuro-symbolic AI and Explainable Machine Learning.
.- Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models.
.- py ciu image: a Python library for Explaining Image Classification with Contextual Importance and Utility.
.- Towards interactive and social explainable artificial intelligence for digital history.
.- XAI & Ethics.
.- Explainability and Transparency in Practice: A Comparison Between Corporate and National AI Ethics Guidelines in Germany and China.
.- The Wildcard XAI: from a Necessity, to a Resource, to a Dangerous Decoy.