ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system
Journal of Automated Reasoning
A substrate for object-oriented interface design
Research directions in object-oriented programming
Design of Knowledge-Based Systems with a Knowledge-Based Assistant
IEEE Transactions on Software Engineering - Special Issue on Artificial Intelligence in Software Applications
Model-based troubleshooting of digital systems
Model-based troubleshooting of digital systems
Model-based reasoning: troubleshooting
Exploring artificial intelligence
Automated generation of model-based knowledge acquisition tools
Automated generation of model-based knowledge acquisition tools
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
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LQMS is a knowledge-based system that identifies and explains anomalies in data acquired from multiple sensors. The knowledge base was built by a sequence of domain experts. Its prototype performed with a high level of accuracy and that performance has been incrementally and significantly improved during development and field testing. Several points are developed in this paper. (1) The combination of an intuitive model (sufficient for the task) and powerful, graphical development tools allowed the domain experts to build a large, high performance system. (2) The Observation. Situation-Relation representation illustrates an intermediate point on the simplicity-expressiveness spectrum, which is understandable to the domain experts, while being expressive enough for the diagnostic task. (3) The system was designed as a workbench for the domain experts. This enticed them to become more directly involved, and, resulted in a better system. (4) The use of an integrated knowledge base edit-tracking system was important to the project in several ways: it reassured computer-naive experts that they could not damage the overall system, which increased their productivity; and, it also allowed experts located in various places around the world to compare, contrast, and integrate changes in a structured way.