Intelligent decision support systems based on SMILE

Author:

Marek J. Druzdzel
Decision Systems Laboratory
School of Information Sciences
and Intelligent Systems Program
University of Pittsburgh
135 North Bellefield Avenue
Pittsburgh, PA 15260, U.S.A.
e-mail: marek@sis.pitt.edu

Abstract:

In this article I share with the readers the basics of and the principles behind intelligent decision support systems based on the theoretically sound principles of probability theory and decision theory. Recent advances in probability theory have led to the development of graphical models that are capable of modeling the causal structure of systems, well understood by human experts, and at the same time give such structure a sound, probabilistic interpretation. Graphical models can serve as a convenient basis for modeling domains that involve high degree of uncertainty and also reasoning with them. Examples of problems that are addressed by systems based on graphical models include computer vision, robotics, pattern matching, medical diagnosis and therapy planning, machine diagnosis, and even on-line help. Microsoft Corporation uses graphical models inside the Windows operating system, in troubleshooting, and in user interfaces, such as on-line Office help and junk mail filtering in Outlook. The graphical models methodology is implemented in a general purpose decision modeling system SMILE and its Windows user interface, GeNIe, developed at the Decision Systems Laboratory. I try to give the readers a flavor of GeNIe models and building systems based on the SMILE library.