Document Type
Conference Proceeding
Publication Date
9-1-2003
Abstract
One approach to integrating first-order logic programming and neural network systems employs the approximation of semantic operators by feedforward networks. For this purpose, it is necessary to view these semantic operators as continuous functions on the reals. This can be accomplished by endowing the space of all interpretations of a logic program with topologies obtained from suitable embeddings. We will present such topologies which arise naturally out of the theory of logic programming, discuss continuity issues of several well-known semantic operators, and derive some results concerning the approximation of these operators by feedforward neural networks.
Repository Citation
Hitzler, P.,
& Seda, A. K.
(2003). Continuity of Semantic Operators in Logic Programming and Their Approximation by Artificial Neural Networks. Lecture Notes in Computer Science, 2821, 355-369.
https://corescholar.libraries.wright.edu/cse/37
DOI
10.1007/978-3-540-39451-8_26
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the 26th Annual German Conference on Advances in Artificial Intelligence, Hamburg, Germany, September 15-18, 2003.
Attached is the unpublished, authors' version of this proceeding. The final, publisher's version can be found at http://dx.doi.org/10.1007/978-3-540-39451-8_26.