The authors provide a coherent explanation of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approach to uncertainty - and offers techniques, based on the belief network, which provides a mechanism for making semantics-based systems operational. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, voice recognition - in short, almost every task requiring that conclusions drawn from certain directions and incomplete information. Probabilistic Reasoning in Intelligent Systems will be interesting for scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and management sciences.