Learning by Analogy: Formulating and Generalizing Plans from Past Experience

摘要

Analogical reasoning is a powerful mechanism for exploiting past experience in planning and problem solving. This paper outlines a theory of analogical problem solving based on an extension to means-ends analysis. An analogical transformation process is developed to extract knowledge from past successful problem solving situations that bear strong similarity to the current problem. Then, the investigation focuses on exploiting and extending the analogical reasoning model to generate useful exemplary solutions to related problems from which more general plans can be induced and refined. Starting with a general analogical inference engine, problem solving experience is, in essence, compiled incrementally into effective procedures that solve various classes of problems in an increasingly reliable and direct manner. (Author)