Introduction to This Special Issue: An Overview of Some Recent Developments in Bayesian Problem Solving Techniques


Peter Haddawy

Decision Systems and Artificial Intelligence Lab Dept. of EE&CS University of Wisconsin­Milwaukee and Intelligent Systems Lab Faculty of Science & Technology Assumption University, Thailand

Abstract

The last few years has seen a surge in interest in the use of techniques from Bayesian Decision Theory to address problems in AI.  Decision Theory provides a normative framework for representing and reasoning about decision problems under uncertainty.  Within the context of this framework, researchers in the Uncertainty in AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases.  The current special issue reviews recent research in Bayesian problem solving techniques.  The articles that follow cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory.  This article provides a brief introduction to Bayesian networks and then covers applications of Bayesian problem solving techniques, knowledge-based model construction and structured representations, and learning of graphical probability models.