Neural-Symbolic Integration

Description

In this thesis, we discuss different techniques to bridge the gap between two different approaches to artificial intelligence: the symbolic and the connectionist paradigm. Both approaches have quite contrasting advantages and disadvantages. Research in the area of neural-symbolic integration aims at bridging the gap between them.
Starting from a human readable logic program, we construct connectionist systems, which behave equivalently. Afterwards, those systems can be trained, and later the refined knowledge be extracted.

Links & Downloads

1. urn:nbn:de:bsz:14-qucosa-25468
2. 314606084
3. https://tud.qucosa.de/id/qucosa%3A25163
4. https://tud.qucosa.de/api/qucosa%3A25163/attachment/ATT-0/
5. https://tud.qucosa.de/api/qucosa%3A25163/attachment/ATT-1/

Tags

info:eu-repo/classification/ddc/004

ddc:004

Additional Fields

Identifer	oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:25163
Date	05 October 2009
Creators	Bader, Sebastian
Contributors	Hölldobler, Steffen, Hammer, Barbara, Technische Universität Dresden
Source Sets	Hochschulschriftenserver (HSSS) der SLUB Dresden
Language	English
Detected Language	English
Type	doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text
Rights	info:eu-repo/semantics/openAccess