Titlebook: Explainable, Transparent Autonomous Agents and Multi-Agent Systems; Second International Davide Calvaresi,Amro Najjar,Kary Fr?mling Confere

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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

Janko Nikolich-?ugich,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

Antarctic

寬度

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

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.

咒語

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

防水

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.