Table-driven Rules in Expert Systems

Pasik, Alexander J.; Schor, Marshall

The structure and organization of expert systems can be usefully modeled after corresponding human experts. Often this modeling degrades because of insufficient expressive power in production system languages. Relational table techniques provide additional abstraction capabilities and are useful in extending the expressiveness of production system rules; the resulting systems can be easier to build, understand and debug because they can reflect more accurately human methods of reasoning. The number of superfluous rules is reduced by organizing much of the problem domain knowledge in relations in working memory. The relational table methods also provide a tool for the interfacing of knowledge bases and databases.


More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-069-83
Published Here
October 25, 2011