Abstract
A soft expert system is defined to be one that is qualitatively fuzzy. In this paper, we present such a system known as "KASER" which stands for 'Knowledge Amplification by Structural Expert Randomization". KASER facilitates reasoning using domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated novel inference engine. It addresses the high cost associated with the knowledge acquisition bottleneck. It also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We demonstrate an application for KASER in the design of an intelligent tutoring system that teaches the basic science of crystal-laser design. It enables the student to experiment with various design concepts and receive feedback on the functionality of the proposed design. This is possible without a need to preprogram all possible scenarios.
| Original language | English |
|---|---|
| Pages (from-to) | 323-329 |
| Number of pages | 7 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 4 |
| State | Published - 2002 |
| Event | 2002 IEEE International Conference on Systems, Man and Cybernetics - Yasmine Hammamet, Tunisia Duration: Oct 6 2002 → Oct 9 2002 |
Keywords
- Intelligent Tutoring Systems
- KASER
- Knowledge Acquisition
- Soft Expert Systems