Abstract
The objective of this paper is to present a new approach to reasoning under uncertainty, based on the use of Bayesian belief networks (BBN’s) enhanced with rough sets. The role of rough sets is to provide additional reasoning to assist a BBN in the inference process, in cases of missing data or difficulties with assessing the values of related probabilities. The basic concepts of both theories, BBN’s and rough sets, are briefly introduced, with examples showing how they have been traditionally used to reason under uncertainty. Two case studies from the authors’ own research are discussed: one based on the evaluation of software tool quality for use in real-time safety-critical applications, and another based on assisting the decision maker in taking the right course of action, in real time, in the naval military exercise. The use of corresponding public domain software packages based on BBN’s and rough sets is outlined, and their application for real-time reasoning in processes under uncertainty is presented.
Highlights
Bayesian Belief Networks (BBN’s) have been widely used in Industrial Information Systems for solving all types of computational problems with insufficient information and uncertainty [1,2]
In general, BBN’s have been very effective, because they allow reasoning and making predictions based on small sets of probabilities with backwards inference, they are still based on probability theory
The objective of this paper is to look at the combination of using BBN’s and rough sets in decision making under uncertainty, and suggest the enhancement of pure Bayesian reasoning by additional use of rough sets for preliminary evaluation of data
Summary
Bayesian Belief Networks (BBN’s) have been widely used in Industrial Information Systems for solving all types of computational problems with insufficient information and uncertainty [1,2] This includes applications such as: water contamination [3], fault detection in an industrial process [4], fog forecasting at the airports [5], predicting software defects [6], inferring certification metrics of software [7], predicting hospital admissions for emergency [8], multisensor fusion for landmine detection [9], evaluation of risk in software development [10], modeling an air traffic control [11], cell signaling pathway modeling [12], reliability estimation [13], safety assessment [14] and risk evaluation [15] in computer-based systems, to name only a few from a long list. The final sections present general conclusions and suggestions for future work
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