Feedback
发表于 2025-3-25 04:25:26
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发怨言
发表于 2025-3-25 11:21:56
David Chen,Bruno Vallespir,Guy Doumeingtsrchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.
jettison
发表于 2025-3-25 14:51:11
Janko Nikolich-Žugich,Anna Lang of explanations about AI system behavior. Our proposed levels of XAI are based on the informational needs of human users, which can be determined using the levels of situation awareness (SA) framework from the human factors literature. Based on our levels of XAI framework, we also propose a method for assessing the effectiveness of XAI systems.
仲裁者
发表于 2025-3-25 17:38:17
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Antarctic
发表于 2025-3-25 20:11:41
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宽度
发表于 2025-3-26 01:31:15
https://doi.org/10.1007/978-94-009-6816-5vide the agent with the cognitive process that is necessary for explainability. Furthermore, the knowledge gathered during the practical reasoning process allows EXPRI to engage in contrastive explanations.
Constrain
发表于 2025-3-26 08:15:45
Handbook on Hyperbaric Medicineknown notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.
咒语
发表于 2025-3-26 11:35:34
Handbook on Hyperbaric Medicine terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. We further identify a set of desiderata for explanations that utilize Theory of Mind. These desiderata inform our belief-based account of explanation.
Mosaic
发表于 2025-3-26 16:12:15
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防水
发表于 2025-3-26 20:22:17
Decision Theory Meets Explainable AIknown notions and methods of Decision Theory. CIU extends the notions of importance and utility for the non-linear models of AI systems and notably those produced by Machine Learning methods. CIU provides a universal and model-agnostic foundation for XAI.