Eliciting and modelling expert knowledge

摘要

This paper evaluates the usefulness of various psychological techniques that can be utilized to elicit and model expert knowledge for subsequent representation in rule-based expert systems. Interviewing, protocol analysis and multidimensional scaling are described and evaluated as complementary methods of knowledge elicitation. In addition ‘context-focusing’ and card-sorting are introduced as short-cut methods for the knowledge engineer's ‘tool box’.It is argued that expert knowledge about uncertainty can be represented as subjective probabilities and that these assessments can (and therefore should) be checked for consistency and coherence as a pre-condition for realism.Finally, the issue of whether it is possible to improve upon expert judgement is discussed and evidence is reviewed which shows that, in repetitive decision-making situations, statistical models of the expert can out-perform the expert on whom the models are based. Statistical modelling has a valid but limited application as a replacement for expert judgement.