This paper focuses on designing expert systems to support decision making in complex, uncertain environments. In this context, our research indicates that strictly probabilistic representations, which enable the use of decision-theoretic reasoning, are highly preferable to recently proposed alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore, we discuss the language of influence diagrams and a corresponding methodology -decision analysis -- that allows decision theory to be used effectively and efficiently as a decision-making aid. Finally, we use RACHEL, a system that helps infertile couples select medical treatments, to illustrate the methodology of decision analysis as basis for expert decision systems.

Decision analysis may be useful to people facing Alzheimer disease (AD) decisions. The use of decision analysis in three such cases is reported. The first case involved a middle-aged person worried about early-onset AD and deciding whether to seek genetic testing. The analysis let the participant reject testing and consider innovative care options. The second case involved a middle-aged person concerned about later-onset AD. The analysis for her was more complex, and led to the assignment of some limited value on genetic testing for her. The third case revolved around a caregiver's treatment decisions for a patient with severe AD. It led her to recognize the importance of factors she had not previously considered. In each of the three cases, the intensive process of decision analysis appears to have improved the subject's decision.

This paper describes the architecture of R&D Analyst, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R&D Analyst interactively guides a user in constructing an influence diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard system perspective. The opportunistic reasoning emphasis of blackboard systems satisfies the flexibility requirements of model construction, thereby suggesting that a similar architecture would be valuable for developing normative decision systems in other domains. Current research is aimed at extending the system architecture to explicitly consider of sequential decisions involving limited temporal, financial, and physical resources.

ABSTRACT This paper presents a risk analysis approach applied to cost-and-schedule estimates for construction projects using Intelligent Decision System (IDS) technology. The paper discusses a pilot cost-and-schedule IDS based on influence diagram technology that contains (1) a deterministic model that links and computes specific schedule and cost factors and (2) a risk breakdown structure that describes the parameters influencing these factors. The paper also describes how a user may validate the deterministic and risk models. It concludes by discussing how the IDS calculates the results using a probabilistic engine and how the results are presented to the user.

An influence diagram is a graphical representation of a decision problem that is at once a formal description of a decision problem that can be treated by computers and a representation that is easily understood by decision makers who may be unskilled in the art of complex probabilistic modeling. The power of an influence diagram, both as an analysis tool and a communication tool, lies in its ability to concisely summarize the structure of a decision problem. However, when confronted with highly asymmetric problems in which particular acts or events lead to very different possibilities, many analysts prefer decision trees to influence diagrams. In this paper, we extend the definition of an influence diagram by introducing a new representation for its conditional probability distributions. This extended influence diagram representation, combining elements of the decision tree and influence diagram representations, allows one to clearly and efficiently represent asymmetric decision problems and provides an attractive alternative to both the decision tree and conventional influence diagram representations.

The ultimate goal of medical computer systems is to help clinicians make good decisions. Such systems must be based on sound principles. Decision analysis is a 25-year-old discipline that provides the needed rigorous foundation for decision assistance. Decision analysis comprises the philosophy, procedures, and tools that can correct the flaws in existing critical care decision-making practice. Intelligent decision systems — computer-based systems that automate decision analysis — make it practical to apply decision analysis to critical care.Orchestra is a pilot intelligent decision system (now under development) that coordinates the efforts of the critical care specialist, the bedside physician, and the bedside nurse in building decision models that can provide recommendations and insight for ventilator management decisions. Decision analysis delivered by intelligent decision systems has great potential for improving critical care decision-making.

This paper focuses on designing expert systems to support decision-making in complex, uncertain environments. In this context, our research indicates that strictly probabilistic representations, which enable the use of decision-theoretic reasoning, are highly preferable to recently proposed alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore, we discuss the language of influence diagrams and a corresponding methodology – decision analysis – that allows decision theory to be used effectively and efficiently as a decision-making aid. Finally, we use RACHEL, a system that helps infertile couples select medical treatments, to illustrate the methodology of decision analysis as a basis for expert decision systems.