Uncertainty Management for Rule-based Decision Support Systems

We present an uncertainty management scheme in rule-based systems for decision making in the domain of urban infrastructure. Our aim is to help end users make informed decisions. Human reasoning is prone to a certain degree of uncertainty but domain experts frequently find it difficult to quantify this precisely, and thus prefer to use qualitative (rather than quantitative) confidence levels to support their reasoning. Secondly, there is uncertainty in data when it is not currently available (missing). In order to incorporate human-like reasoning within rule-based systems we use qualitative confidence levels chosen by domain experts in urban infrastructure. We introduce a mechanism for the representation of confidence of input facts and inference rules, and for the computation of confidence in the inferred facts. We also present a mechanism for computing inferences in the presence of missing facts, and their effect on the confidence of inferred facts.