Recent artificial intelligence models of analogical reasoning are based on mapping some underlying causal network of relations between analogous situations. However, causal realtions relevant for the purpose of one analogy may be irrelevant for another. We describe here a technique wich uses an explicit representation of the putpose of the analogy to automatically create the relevant causal network. We illustrate the technique with two case studies in which concepts of everyday artifacts are learned by analogy
