Automating knowledge acquisition for generic tasks

Abstract

Research into knowledge acquisition is becoming increasingly important. One of the main aims of this research is to improve both the speed with which a knowledge-based system can be constructed, and the quality of the resulting knowledge base. Increasing the level of automation of the knowledge process has produced promising results, but has also raised new problems. This paper describes a new approach to the automation of knowledge acquisition which solves some of these problems. The paper splits the knowledge acquisition process into several distinct stages and uses this split as a basis for classifying automated knowledge-acquisition tools. The problems with each type of tool are explored, before a new approach to the knowledge-acquisition automation problem is proposed, based on generic tasks. The concept of the generic task has played an important role in furthering research into second-generation knowledge-based systems. To date, all the knowledge acquisition for generic tasks has been performed manually. If an automated knowledge-acquisition tool existed for each generic task, then this would facilitate the modular construction of knowledge-based systems. This is illustrated through an example knowledge-acquisition tool, which elicits knowledge for the Functional Reasoning generic task.