@inbook{0d4ef6ffcef1464198a9e0fc3888b075,

title = "Connectionist Representation of Multi-Valued Logic Programs",

abstract = "H{\"o}lldobler 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.",

author = "Ekaterina Komendantskaya and Maire Lane and Seda, {Anthony Karel}",

year = "2007",

doi = "10.1007/978-3-540-73954-8_12",

language = "English",

isbn = "9783540739531",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "283--313",

editor = "Barbara Hammer and Pascal Hitzler",

booktitle = "Perspectives of Neural-Symbolic Integration",

}