Towards a Taxonomy of Problem Solving Types

TLDR

Expert systems are traditionally built around a shared knowledge base accessed by generic problem‑solvers, but our work proposes decomposing domain knowledge into specialized substructures, a need highlighted by the incomplete nature of novice expert systems. The study proposes that distinct problem‑solving types correspond to separate specialized substructures within an expert system. Each substructure is hierarchically organized into specialists that differ by domain content—e.g., heart disease versus liver—while performing the same problem‑solving type. The framework shows that knowledge is distributed among problem‑.

Abstract

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 that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist 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 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 novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.