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E-raamat: Explainable and Transparent AI and Multi-Agent Systems: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, Revised Selected Papers

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Explainable and Transparent AI and Multi-Agent Systems: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, Revised Selected PapersSuurem pilt
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This volume LNCS 14127 constitutes the refereed proceedings of the 5th International Workshop, EXTRAAMAS 2023, held in London, UK, in May 2023.  The 15 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions. The workshop focuses on Explainable Agents and multi-agent systems; Explainable Machine Learning; and Cross-domain applied XAI.





 
Explainable Agents and multi-agent systems.- Mining and Validating
Belief-based Agent Explanations.- Evaluating a mechanism for explaining BDI
agent behaviour.- A General-Purpose Protocol for Multi-Agent based
Explanations.- Dialogue Explanations for Rules-based AI Systems.- Estimating
Causal Responsibility for Explaining Autonomous Behavior.- Explainable
Machine Learning.- The Quarrel of Local Post-hoc Explainers for Moral Values
Classification in Natural Language Processing.- Bottom-Up and Top-Down
Workflows for Hypercube- and Clustering-based Knowledge
Extractors.- Imperative Action Masking for Safe Exploration in Reinforcement
Learning.- Reinforcement Learning in Cyclic Environmental Change for
Non-Communicative Agents: A Theoretical Approach.- Inherently Interpretable
Deep Reinforcement Learning through Online Mimicking.- Counterfactual,
Contrastive, and Hierarchical Explanations with Contextual Importance and
Utility.- Cross-domain applied XAI.- Explanation Generation via
Decompositional Rules Extraction for Head and Neck Cancer
Classification.- Metrics for Evaluating Explainable Recommender
Systems.- Leveraging Imperfect Explanations for Plan Recognition
Problems.- Reinterpreting Vulnerability to Tackle Deception in
Principles-Based XAI for Human-Computer Interaction.- Using Cognitive Models
and Wearables to Diagnose and Predict Dementia Patient Behaviour.
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