Proceedings of the Annual Meeting of the Cognitive Science Society

Macro-operators and chunks have long been used to model the acquisition and refinement of procedural knowledge. However, it is clear that human learners use more sophisticated techniques to encode more powerful operators than simple linear macro-operators: specifically, linear macro-operators cannot represent arbitrary repetitions of operators. This paper presents a process-model for the acquisition of iterative macrooperators, which are an efficient representation of repeating operators. W e show that inducing iterative macro-operators from empirical problem-solving traces provides dramatically better efficiency results than simple linear macro-operators. This domain-independent learning mechanism is integrated into the FERMI problemsolver, giving more evidence that humans have a similar learning capability.