The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
Directions for AI in the eighties
ACM SIGART Bulletin
Computer
EXPERT: a system for developing consultation models
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 2
Extending a knowledge-based system to deal with ad hoc constraints
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
The declarative representation and procedural simulation of causality in physical mechanisms
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
The origin and resolution of ambiguities in causal arguments
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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Existing expert systems have a high percentage agreement with experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the Integrated Diagnostic Model (IDM), that integrates two sources of knowledge, a shallow, reasoning-oriented, experiential knowledge base and a deep, functionally-oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair, an implementation in the mechanical domain is described, as well as an example in the medical domain.