The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Authors

Sebastian Bader
Steffen Holldobler
Pascal HitzlerFollow

Document Type

Conference Proceeding

Publication Date

1-1-2004

Abstract

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.

Comments

Presented at the Third International Conference on Information, Tokyo, Japan, November/December 2004.

Repository Citation

Bader, S., Holldobler, S., & Hitzler, P. (2004). The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence. , 22-33.
https://corescholar.libraries.wright.edu/cse/127