On capturing human skills and knowledge; algorithmic approaches to model identification

Abstract

Algorithmic identification of models of human skills and knowledge is considered. Alternative representational forms are discussed in terms of variables, relationships among variables, and parameters within relationships. A key distinction is between models of signal processing and models of symbol processing. Methods of identification for these two classes of models are discussed and contrasted. Identifiability, or the uniqueness of models identified, is considered for both classes of model, and a variety of fundamental limits relating to existence and computability are reviewed. Practical issues and results associated with identification are considered in the context of three examples of identifying signal processing and symbol processing models. The discussion of available methods and known limits concludes with consideration of the general implications for endeavors aimed at describing and explaining human skills and knowledge.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>