The recent proliferation of expert system development projects has focused the attention of the AI community on the problem of knowledge acquisition. Traditional programming skills possessed by the majority of knowledge engineers are found to be of little use when faced with the problem of extracting potential knowledge base information from a human expert. This paper deals with those topics and situations which have so often been described as the 'bottleneck' of expert system development. The concepts of expert and expertise are explored; the paradox of expertise is examined. Those interviewing methods found to be most effective for knowledge acquisition are discussed. A broad gamut of knowledge acquisition considerations are covered in detail including knowledge base structure, conversion of knowledge into rules, and refinement of the knowledge base.
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
- Computer Science Applications