Probabilistic Decision Support System: Probabilistic Decision Support System
From DSL
The principles of decision-analytic decision support, implemented in SMILE can be applied in practical decision support systems (DSSs). In fact, Decision Systems Laboratory both has developed and is developing several such probabilistic DSSs in which GeNIe plays the role of a developer's environment and SMILE plays the role of the reasoning engine. A decision support system based on SMILE can be equipped with a dedicated user interface.
Probabilistic DSSs are a new generation of systems that are capable of modeling any real-world decision problem using theoretically sound and practically invaluable methods of probability theory and decision theory. Based on graphical representation of the problem structure, these systems allow for combining expert opinions with frequency data, gather, manage, and process information to arrive at intelligent solutions.
Probabilistic DSSs are based on a philosophically different principle than rule-based expert systems. While the latter attempt to model the reasoning of a human expert, the former use an axiomatic theory to perform computation. The soundness of probability theory provides a clear advantage over rule-based systems that usually represent uncertainty in an ad-hoc manner, such as using certainty factors, leading to under-responsiveness or over-responsiveness to evidence and possibly incorrect conclusions.
Probabilistic DSSs are applicable in many domains, among others in medicine (e.g., diagnosis, therapy planning), banking (e.g., credit authorization, fraud detection), insurance (e.g., risk analysis, fraud detection), military (e.g., target detection and prioritization, battle damage assessment, campaign planning), engineering (e.g., process control, machine and process diagnosis), and business (e.g., strategic planning, risk analysis, market analysis).
An example DSS developed using GeNIe and SMILE: is the medical diagnostic system Hepar II (Onisko et al. 1997, 1998). The system aids physicians in diagnosis of liver disorders. The structure of the model, currently consisting of over 70 variables, has been elicited from physician experts, while its numerical parameters have been learned from a database of patient cases.
