Explainable and Transparent AI and Multi-Agent Systems

Explainable and Transparent AI and Multi-Agent Systems

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

Ciatto, Giovanni; Carli, Rachele; Omicini, Andrea; Najjar, Amro; Aydogan, Reyhan; Fraemling, Kary; Hulstijn, Joris; Calvaresi, Davide

Springer International Publishing AG

11/2024

240

Mole

9783031700736

Pré-lançamento - envio 15 a 20 dias após a sua edição

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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.
Computer Science;Informatics;Conference Proceedings;Research;Applications;multi-agent systems;computing most probable explanation;machine learning;law, social and behavioral sciences;artificial intelligence;knowledge representation and reasoning;rule learning