Explainable and Transparent AI and Multi-Agent Systems 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, Revised Selected Papers /
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 Agent...
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| Соавтор: | |
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| Другие авторы: | , , , , , , , |
| Формат: | Электронный ресурс eКнига |
| Язык: | английский |
| Опубликовано: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2023.
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| Редактирование: | 1st ed. 2023. |
| Серии: | Lecture Notes in Artificial Intelligence,
14127 |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/978-3-031-40878-6 |
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Оглавление:
- 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.