Learning and memorizing models of logical theories in a hybrid learning device

Autor(en): Gust, H.
Kühnberger, K.-U. 
Geibel, P.
Stichwörter: Learning systems; Network protocols; Neural networks; Sensor networks, First-order; Hybrid learnings; Learning models; Logical theories; Neural Network learnings; Symbolic reasonings, Education
Erscheinungsdatum: 2008
Enthalten in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band: 4985 LNCS
Ausgabe: PART 2
Startseite: 738
Seitenende: 748
Zusammenfassung: 
Although there are several attempts to resolve the obvious tension between neural network learning and symbolic reasoning devices, no generally acceptable resolution of this problem is available. In this paper, we propose a hybrid neuro-symbolic architecture that bridges this gap (in one direction), first, by translating a first-order input into a variable-free topos representation and second, by learning models of logical theories on the neural level by equations induced by this topos. As a side-effect of this approach the network memorizes a whole model of the training input and allows to build the core of a framework for integrated cognition. © 2008 Springer-Verlag Berlin Heidelberg.
Beschreibung: 
Conference of 14th International Conference on Neural Information Processing, ICONIP 2007 ; Conference Date: 13 November 2007 Through 16 November 2007; Conference Code:73944
ISBN: 9783540691594
ISSN: 03029743
DOI: 10.1007/978-3-540-69162-4_77
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-54049109676&doi=10.1007%2f978-3-540-69162-4_77&partnerID=40&md5=87367626717f370cbbd291528facf62c

Show full item record

Page view(s)

3
Last Week
0
Last month
2
checked on Jun 16, 2024

Google ScholarTM

Check

Altmetric