Connectionist Representation of Multi-Valued Logic Programs

Ekaterina Komendantskaya, Maire Lane, Anthony Karel Seda

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward artificial neural network may be constructed, using only binary threshold units, which can compute the familiar immediate-consequence operator TP associated with P. In this chapter, essentially these results are established for a class of logic programs which can handle many-valued logics, constraints and uncertainty; these programs therefore represent a considerable extension of conventional propositional programs. The work of the chapter basically falls into two parts.
Original languageEnglish
Title of host publicationPerspectives of Neural-Symbolic Integration
EditorsBarbara Hammer, Pascal Hitzler
PublisherSpringer
Pages283-313
Number of pages31
ISBN (Electronic)9783540739548
ISBN (Print)9783540739531
DOIs
Publication statusPublished - 2007

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Berlin Heidelberg
Volume77
ISSN (Print)1860-949X

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