Proceedings of the Annual Meeting of the Cognitive Science Society

An intelligent agent which is involved in a variety of cognitive tasks must be able to learn new methods for performing each of them. W e discuss how this can be achieved by a system composed of sets of rules for each task. To learn a new rule, the system first isolates the rule set which should be augmented, and then invokes an explanation-based learning mechanism to construct the new rule. This raises the question of how appropriate target concepts for explanation can be determined for each task. We discuss the solution to this problem employed in the CASTLE system, which retrieves target concepts in the form of performance specifications of its components, and demonstrate the system learning rules for several different teisks using this uniform mechanism.