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cs.AI2026
Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
Daniele Fossemò, Filippo Mignosi, Giuseppe Placidi +3
In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learning user preferences. We speci…
cs.AI2022★ 1 cited
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems
Fabio Aurelio D'Asaro, Luca Raggioli, Salim Malek +2
We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the…