| Abstract: | The theme of this article is that knowledge acquisition can be managed as a transition from general expertise to specific expertise. FORR is a general architecture for learning and problem solving that models expertise at a set of related problem classes. This architecture postulates initial brood domain knowledge, and gradually specializes it to simulate expertise in individual problem classes. FORR is based upon a realistic portrayal of the nature of human expertise and its application. Rather than restrict learning to a single method or a single kind of knowledge, the architecture pragmatically requires multiple, disagreeing heuristic agents to collaborate on decisions. A FORR-based program learns both from its apprenticeship to an external expert model and from practice in its domain. An implementation for game playing is described that raises interesting issues about the organization and modification of conflicting expertise, and the role that experience plays in such learning. FORR's principal strengths are its smooth integration of multiple expertise, its ability to learn many ways, its tolerance for human and machine error, its graceful degradation, its transparency, and its support for a developmental paradigm. |
