From Common Sense Reasonig to Neural Network Models: a Conditional and Multi-preferential Approach for Explainability and Neuro-Symbolic Integration
Contributo in Atti di convegno
Data di Pubblicazione:
2022
Abstract:
This short paper reports about a line of research exploiting a conditional logic of commonsense reasoning to provide a semantic interpretation to neural network models. A “concept-wise" multi-preferential semantics for conditionals is exploited to build a preferential interpretation of a trained neural network starting from its input-output behavior. The approach is general (model agnostic): it is based on a notion of metric distance to define preferences and has been first proposed for Self-Organising Maps (SOMs). For MultiLayer Perceptrons (MLPs), a deep network can as well be regarded as a (fuzzy) conditional knowledge base (KB), in which the synaptic connections correspond to weighted conditionals. This opens to the possibility of adopting conditional description logics as a basis for neuro-symbolic integration. Proof methods for many-valued weighted conditional KBs have been developed, based on Answer Set Programming and Datalog encodings to deal with the entailment and model-checking problems.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
Preferential Description Logics, Typicality, Neural Networks, Explainability
Elenco autori:
Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Valentina Gliozzi ,Daniele Theseider Dupré
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Link al Full Text:
Titolo del libro:
Proceedings of the 8th Workshop on Formal and Cognitive Reasoning co-located with the 45th German Conference on Artificial Intelligence (KI 2022)
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