Case-based reasoning

[1][2] In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.

A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning.

It does require à priori domain knowledge that is gleaned from past experience which established connections between symptoms and causes.

Diagnosis of a problem transpires as a rapid recognition process in which symptoms evoke appropriate situation categories.

However, the strategy won't work independently with truly novel problems, or where deeper understanding of whatever is taking place is sought.

An alternative approach to problem solving is the topographic strategy which falls into the category of deep reasoning.

Topography in this context means a description or an analysis of a structured entity, showing the relations among its elements.

However, all inductive reasoning where data is too scarce for statistical relevance is inherently based on anecdotal evidence.

CBR traces its roots to the work of Roger Schank and his students at Yale University in the early 1980s.

Schank's model of dynamic memory[10] was the basis for the earliest CBR systems: Janet Kolodner's CYRUS[11] and Michael Lebowitz's IPP.