DOI:
Abstract:
Learning reusable sequences can support the development of expertise in many domains, either by improving decisionmaking quality or decreasing execution speed. This paper introduces and evaluates a method to learn action sequences for generalized states from prior problem experience. From experienced sequences, the method induces the context that underlies a sequence of actions. Empirical results indicate that the sequences and contexts learned for a class of problems are actually those deemed important by experts for that particular class, and can be used to select appropriate action sequences when solving problems there.