יובל שחר

PROTÉGÉ is a meta-level program that generates knowledge-acquisition tools that are based on the method of skeletal-plan refinement. In this paper, we propose a flexible and extensible architecture that allows the problem-solving method to be assembled from more basic methods. In this architecture, we emphasize (1) a uniform view of problem solving at different levels of granularity, (2) an explicit data model that allows construction of complex datatypes from predefined datatypes and (3) the inclusion of domain-dependent control information within a domain-independent problem-solving method. We show how such a model of problem solving can drive the generation of knowledge-acquisition tools.