ABSTRACT
Past research in cognitive science reveals that prototypical configurations of domain objects, called perceptual-chunks, underlie the abilities of experts to solve problems efficiently. Little research, however, has been carried out on the mechanism used for learning perceptual-chunks from solving problems. The present paper addresses this issue in the domain of geometry proof problem-solving. We have developed a computational model that chunks, from problem diagrams, configuration of the elements which are visually grouped together, based on perceptual chunking criterion. This criterion, called recognition rules, reflects how people see problem diagrams and thus works effectively to determine which portion of problem diagrams are more likely to be grouped as a chunk. This distinguishes the proposed method from the goal-oriented chunking techniques used in machine-learning community. Experiments on solving geometry problems show that our technique can detect essential diagram configurations common to many problems. Additionally, implications of the recognition rules are discussed from a cognitive point of view.
