Towards a taxonomy of problem-solving types

Our group’s work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures We propose that there exist different problem-solving types, i e, uses of knowledge, and corresponding to each is a separate substructure specializing in that1 type of problem-solving Each substructure is in turn further decomposed into a hierarchy of specialists which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e g, one of them may specialize in “heart disease,” while another may do so in “liver, ” though both of them are doing the same type of problem-solving Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver We have so far had occa-sion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions In a novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete the