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 -