Proceedings of the Annual Meeting of the Cognitive Science Society

Much of expertise in problem-solving situations involves rajiidly choosinj? a tightly-constrained schema that is appropriate to the current problem. The paradigm of explanation-based learning, is being applied to investigate hou- an intelligent system can acquire these "appropriately general" schemata. While the motivations for producing these specialized schemata are computational, results reported in the psychological literature are corroborated by a fully-implemented comjjuter model. .Acquiring these special case schemata involves combining schemata from two different classes. One class contains domain-independent problem solving schemata, while the other class consists of domain-specific knowledge. By analyzing solutions to sample problems, new domain knowledge is produced that often is not easily usable by the problem-solving schemata. .Special case schemata result from constraining these general schemata so that a k n o w n problem solving technique is guaranteed to work. This significantly reduces the amount of ])lanning that the problemsolver would other>*ise need to perlorm elaborating the general schema in a n e w problem-solving situation.The model and an application of it in the domain of classical physics are presented.