Weighted defeasible knowledge bases and a multipreference semantics for a deep neural network model
Artificial Intelligence
2021-01-26 v2 Neural and Evolutionary Computing
Abstract
In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a "concept-wise" multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons.
Keywords
Cite
@article{arxiv.2012.13421,
title = {Weighted defeasible knowledge bases and a multipreference semantics for a deep neural network model},
author = {Laura Giordano and Daniele Theseider Dupré},
journal= {arXiv preprint arXiv:2012.13421},
year = {2021}
}
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23 pages