Intelligent decision support systems

This paper explores the basic ingredients of intelligent decision support systems in partial contrast to approaches followed by expert systems. Rule based expert systems for decision support have been successful for well structured, well understood decision situations of a taxonomic classification type. But, in general, A.I. has growing influence in software engineering for ill-structured application areas by supporting an incremental development process with new programming techniques and architectures. As uncertainty is prevalent, information costly and payoff relevant, and the preferred solution depends on the specific beliefs and preferences of an individual or group decision maker. The resolution methods of decision theory embodied in first-order predicate logic, form a natural basis for computerized intelligent decision support. A unified characterization of knowledge and inference for logical, probabilistic, and decision-theoretic reasoning is developed for intelligent decision support over a wide spectrum of decision situations.