One of the outstanding problems facing designers of expert systems pertains to the capture of human expertise for replay by the system. The scaling of such expert or knowledge-based systems implies a capability for natural language situational entry as well as a meta-rule based system for reasoning by analogy. The former capability provides for the semantic normalization of natural language, while the latter capability employs metaphor to expand the derived rule base - thereby enabling it to fail softly.
['Stuart H. Rubin']
A Learning-by-Metaphor Human-Machine System