Explainable and Transparent AI and Multi-Agent Systems [electronic resource] : 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6-10, 2024, Revised Selected Papers / edited by Davide Calvaresi, Amro Najjar, Andrea Omicini, Reyhan Aydogan, Rachele Carli, Giovanni Ciatto, Joris Hulstijn, Kary Främling.

Colaborador(es): Calvaresi, Davide [editor.] | Najjar, Amro [editor.] | Omicini, Andrea [editor.] | Aydogan, Reyhan [editor.] | Carli, Rachele [editor.] | Ciatto, Giovanni [editor.] | Hulstijn, Joris [editor.] | Främling, Kary [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Lecture Notes in Artificial Intelligence ; 14847Editor: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: X, 243 p. 79 illus., 70 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031700743Tema(s): Multiagent systems | Machine learning | Compilers (Computer programs) | Natural language processing (Computer science) | Computer science | Computers, Special purpose | Multiagent Systems | Machine Learning | Compilers and Interpreters | Natural Language Processing (NLP) | Theory of Computation | Special Purpose and Application-Based SystemsFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.30285436 Clasificación LoC:QA76.76.I58Recursos en línea: Libro electrónicoTexto
Contenidos:
-- 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.
En: Springer Nature eBookResumen: 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.
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-- 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.

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.

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