Sciweavers

Share
FLAIRS
2003

Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study

8 years 8 months ago
Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study
Neural-symbolic integration concerns the integration of symbolic and connectionist systems. Distributed knowledge representation is traditionally seen under a purely symbolic perspective. In this paper, we show how neural networks can represent symbolic distributed knowledge, acting as multiagent systems with learning capability (a key feature of neural networks). We then apply our approach to the well-known muddy children puzzle, a problem used as a testbed for distributed knowledge representation formalisms. Finally, we sketch a full solution to this problem by extending our approach to deal with knowledge evolution over time.
Artur S. d'Avila Garcez, Luís C. Lamb, Krys
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where FLAIRS
Authors Artur S. d'Avila Garcez, Luís C. Lamb, Krysia Broda, Dov M. Gabbay
Comments (0)
books