An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge bases

Laura Giordano; Daniele Theseider Dupré

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arXiv · 2202.01123

An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge bases

Laura Giordano·Daniele Theseider Dupré

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Abstract

Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of MultiLayer Perceptrons (MLPs). In this paper we consider weighted conditional ALC knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment LC of ALC we exploit ASP and "asprin" for reasoning with the concept-wise multipreference entailment under a phi-coherent semantics, suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs. The paper is under consideration for acceptance in TPLP.

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BibTeX RIS

Laura Giordano, Daniele Theseider Dupré. 2022-02-02. An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge bases. https://arxiv.org/abs/2202.01123

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