Foundations of connectionist-symbolic integration:
representation, paradigms, and algorithms

Berlin, August 21, 2000

In this workshop, we aim at fostering a deep discussion about at least three topics that we believe to be fundamental for the development of a successful theory of Connectionist-Symbolic Integration: representation, paradigms, and algorithms. Concerning representation, it is fully recognized that structured representations are ubiquitous in different fields such as knowledge representation, language modeling and pattern recognition. The interest in developing connectionist architectures capable of dealing with these rich representations (as opposed to "flat" or vector-based representations) can be traced back to the end of the 80's. Today, after more than ten years since the explosion of interest in connectionism, research in architectures and algorithms for learning structured representations still has a lot to explore and no definitive answers have emerged.

Different integration paradigms have also been proposed: these are the unified and the hybrid approaches to integration. Whereas the purely connectionist ("connectionist-to-the-top") approach claims that complex symbol processing functionalities can be achieved via neural networks alone, the hybrid approach is premised on the complementarity of the two paradigms and aims at their synergistic combination in systems comprising both neural and symbolic components. In fact, these trends can be viewed as two ends of an entire spectrum.

Topics of interest include:

Other researchers interested in attending the workshop without contributing a paper should send a short position paper describing their interest in the mentioned topics.