Abstract

This paper describes work on the construction of a configurable knowledge acquisition tool, Jigsaw. Unlike automated knowledge acquisition programs such as MORE [Kahn, 1988], MOLE [Eshelman, 1988], and OPAL [Musen, 1989], each of which automates elicitation for just one problem solving method, it is possible to alter Jigsaw's knowledge acquisition strategy to match different problem solving methods.

The work is based upon eliciting knowledge for problem solvers made up from different combinations of generic task (as denned in [Chandrasekaran, 1986] and [Chandrasekaran, 1988]). Each combination of generic tasks defines the functionality of a different problem solving method. However, the eventual aim of this work is that it will be possible to adapt it to a range of different KADS [Schreiber et. al., 1987] interpretation models and thus it will be part of a complete knowledge acquisition methodology.

The paper outlines the requirements for such a knowledge acquisition tool and details the distributed architecture which allows the tool, Jigsaw, to achieve the required flexibility to elicit knowledge for such problem solvers. An important part of this flexibility is the way in which Jigsaw can be configured to match different types of problem solver. This is described in some detail.

Jigsaw has been used to reproduce the MDX2 [Sticklen, 1987] knowledge base, which was initially constructed by using manual knowledge acquisition techniques. The paper gives a description of how Jigsaw elicited this knowledge.