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.
|Title of host publication||Perspectives of neural-symbolic integration|
|Editors||Barbara Hammer, Pascal Hitzler|
|Place of Publication||Berlin|
|Number of pages||31|
|Publication status||Published - 2007|
|Name||Studies in computational intelligence|