Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground:...

With rapidly growing interest in the development of knowledge-based computer consulting systems for various problem domains, the difficulties associated with knowledge acquisition have special importance. This paper reports on the results of experiments designed to assess the effectiveness of an...

This research is part of a larger effort to build machine-based tools for developing scientific theories. In analogy with the research process in empirical research, we describe a logical cycle of theory development: (1) starting with an informal version of a theory, (2) then moving to its...

Expert systems often employ a weight on rules to capture conditional probabilities. For example, in classic rule-based settings, Pr(h | e) = x is used to mean "If e is known to be true then conclude h is true with probability x." Further, other probability-based approaches, such as influence...

This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear...

This note offers an extension of Tam and Kiang (Tam, K. Y., M. Y. Kiang. 1992. Management applications of neural networks: The case of bank failure predictions. Management Sci. 38(7) 926--947.). First the weakness of the standard back propagation neural network learning algorithm is discussed,...

AI automated plan synthesis programs ("planners") typically represent plans as a partially ordered network whose nodes are instants in time and whose arcs are precedence constraints. Such representations are essentially PERT charts. This paper provides an introduction to AI planners and...

Outcome prediction based on historical data has been of practical and theoretical interest in many disciplines. A common type of outcome prediction is binary or discrete outcome prediction, as found in medical diagnosis and firm bankruptcy prediction. The prediction problem studied in this paper...

Most induction algorithms for building predictive models take as input training data in the form of feature vectors. Acquiring the values of features may be costly, and simply acquiring all values may be wasteful or prohibitively expensive. Active feature-value acquisition (AFA) selects features...