TY - CHAP

T1 - Connectionist representation of multi-valued logic programs

AU - Komendantskaya, Ekaterina

AU - Lane, Maire

AU - Seda, Anthony Karel

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=34548129216&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-73954-8_12

DO - 10.1007/978-3-540-73954-8_12

M3 - Other chapter contribution

AN - SCOPUS:34548129216

SN - 9783540739531

T3 - Studies in computational intelligence

SP - 283

EP - 313

BT - Perspectives of neural-symbolic integration

A2 - Hammer, Barbara

A2 - Hitzler, Pascal

PB - Springer

CY - Berlin

ER -