An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge bases
| Autor: | LAURA GIORDANO, DANIELE THESEIDER DUPRÉ |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Artificial Intelligence (cs.AI) I.2.4 Computational Theory and Mathematics Artificial Intelligence Hardware and Architecture Computer Science - Artificial Intelligence Computer Science::Logic in Computer Science Computer Science::Neural and Evolutionary Computation Computer Science::Programming Languages Software Theoretical Computer Science 68T27 |
| Popis: | 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. Paper presented at the 38th International Conference on Logic Programming (ICLP 2022), 16 pages |
| Databáze: | OpenAIRE |
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