4 papers
Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +4
Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning. While initial NeSy approaches have targeted mainly symbolic…
Logic of Hypotheses: from Zero to Full Knowledge in Neurosymbolic Integration
Davide Bizzaro, Alessandro Daniele
Neurosymbolic integration (NeSy) blends neural-network learning with symbolic reasoning. The field can be split between methods injecting hand-crafted rules into neural models, and…
T-ILR: a Neurosymbolic Integration for LTLf
Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +3
State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle te…
Gradient-Based Optimization on Gödel Logic as Discrete Local Search
Alessandro Daniele, Emile van Krieken
A fundamental challenge in neurosymbolic systems is applying continuous gradient-based optimization to discrete logical domains. While fuzzy relaxations provide differentiability,…