Experience of constructing a fault localisation expert system using an AI toolkit

Abstract

The paper briefly outlines the operation of community clubs, a method of organising collaborative research which has proved effective within the UK government's Alvey programme, and focuses on one of the projects organised by the DAPES (the Data Processing Expert Systems) club: namely an expert system for localising a fault within a network of data communications equipment. Coming from a DP background, club members were particularly interested in methodical approaches to the implementation, maintenance and documentation of expert systems. They chose to construct an intermediate representation based on the KADS methodology (which is briefly described), which was then used as the basis of two parallel implementations.The paper concentrates on the description one of these implementations, which used a specialised AI toolkit (Inference ART). It describes the performance of the final system and the way it is constructed. It also comments on the performance offered by the toolkit, and compares the system with a second implementation carded out in parallel using a PC-based shell.

Choosing a reasoning style for knowledge based system: lessons from supporting a help desk

“…is represented as conditionaction rules. Examples of such systems applied to the HD advice-giving domain include Abraham et al (1991), Inder (1989a), Taylor (1985) and Register and Rewari (1991). However, it is obvious that rules are but one way of capturing the knowledge of concepts and their inter-relationships needed for a domain model.…”