Explainable and Transparent AI and Multi-Agent Systems: 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6–10, 2024, Revised Selected Papers: Lecture Notes in Computer Science, cartea 14847
Editat de Davide Calvaresi, Amro Najjar, Andrea Omicini, Reyhan Aydogan, Rachele Carli, Giovanni Ciatto, Joris Hulstijn, Kary Främlingen Limba Engleză Paperback – 20 sep 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.
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Specificații
ISBN-13: 9783031700736
ISBN-10: 3031700732
Pagini: 243
Ilustrații: XX, 234 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.38 kg
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3031700732
Pagini: 243
Ilustrații: XX, 234 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.38 kg
Ediția:2024
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Cham, Switzerland
Cuprins
.- 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.
.- 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.