Learning by understanding analogies

摘要

Analogical inference is a process which proposes new conjectures about a target analogue based on facts known about a source analogue. This article formally defines this process and discusses how to efficiently guide it to the conjectures which can help to solve a given problem. The intuition that a useful analogy provides the information needed to solve the problem, and no more, leads to two sets of heuristics: one set based on abstractions—abstract relations which encode solutions to previous problems—and the second, based on a preference for the most general set of new conjectures. Experimental data, collected using a program which embodies this theory of analogy, confirms the effectiveness of these ideas.