Flexible Strategy Learning: Analogical Replay of Problem Solving Episodes

Machine Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 12

Control Learning

This paper describes the integration of analogical reasoning into general problem solving as a method of learning at the strategy level to solve problems more effectively. Learning occurs by the generation and replay of annotated derivational traces of problem solving episodes. The problem solver is extended with the ability to examine its decision cycle and accumulate knowledge from the chains of successes and failures encountered during its search experience. Instead of investing substantial effort deriving general rules of behavior to apply to individual decisions, the analogical reasoner compiles complete problem solving cases that are used to guide future similar situations. Learned knowledge is flexibly applied to new problem solving situations even if only a partial match exists among problems. We relate this work with other alternative strategy learning methods, and also with plan reuse. We demonstrate the effectiveness of the analogical replay strategy by providing empirical results on the performance of a fully implemented system, PRODIGY/ANALOGY, accumulating and reusing a large case library in a complex problem solving domain.