Modeling practical reasoning

Knowledge modeling perspectives on knowledge acquisition suggest that it is reasonable to analyze knowledge bases as collections of models. This paper focuses on those parts of the knowledge base that model the practical reasoning processes of human experts, and asks what properties those models might be expected to have. It surveys the general notion of a model and its connotations in information systems science. It analyzes the structure of practical reasoning as a control process with information flows involving essential uncertainty and adaptivity, where robustness is more significant than optimality. Systemic considerations suggest that models of the relevant knowledge will consist of a collection of isolated productions, many of which will be concerned with avoidance rather than goal achievement. The relations of such models to role limiting methods, generic tasks and deep knowledge are discussed. Finally, tthe implications of the knowledge modeling perspectives for developments in knowledge acquisition are discussed.