Uncertainty Management for Rule-Based Decision Support Systems

Abstract

Abstract-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.

ATU-DSS: Knowledge-Driven Data Integration and Reasoning for Sustainable Subsurface Inter-asset Management

“…The retrieved data is displayed on the user interface and fed into the rule engine for automated reasoning of potential consequences. The uncertainty of facts and rules are also propagated during the reasoning process [5]. Once this process finishes, potential consequences are identified from the inferred facts and presented to users according to their estimated severity and likelihood.…”

“…In the ATU-DSS, in cases where real data is missing (i.e. default values under worst case assumption are used) in the reasoning process [5], the system will not only remind users of the missing data but also suggest suitable investigation techniques to get the missing data (based on the ATU Investigation Ontology). Users can choose to accept the default value or add/update data after some investigation.…”