Building expert systems
Knowledge-Based Systems in Artificial Intelligence: 2 Case Studies
Knowledge-Based Systems in Artificial Intelligence: 2 Case Studies
Using empirical analysis to refine expert system knowledge bases (seek)
Using empirical analysis to refine expert system knowledge bases (seek)
NEOMYCIN: reconfiguring a rule-based expert system for application to teaching
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
DART: an expert system for computer fault diagnosis
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
SACON: a knowledge-based consultant for structural analysis
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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This paper describes ROGET, a knowledge-based system that assists a domain expert with an Important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain Independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.