Abstract:

Our group's work in medical decision making has lead us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures - there exist different problem solving types, i.e., uses of knowledge, and corresponding to each is a seperate substructure specialising in that type of problem solving. Each substructure is in turn further decomposed into a hierachy 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 specialise 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 who know how best 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 base is a problem solver i.e have so far had occasion 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 them.