Probabilistic Models for Uncertainty Management in Expert Systems with Applications to Fossil Power Plants
A L J Valverde, Stephen M. Gehl · Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · 1992
Human experts and decision makers must typically plan and act without the benefit of complete knowledge and information. In the theory and application of expert systems, uncertainty in human knowledge and reasoning must be effectively managed in order for such systems to be successful in complex, real-world domains. In this paper two probabilistic models for uncertainty management in expert systems are considered: (a) Bayesian probability theory and (b) Dempster-Shafer belief theory. The conceptual foundations of each theory are first summarized and are then applied to a typical diagnostic inference problem drawn from the fossil plant performance area. The advantages and disadvantages associated with each approach are then discussed, concluding with some general guidelines for selecting an appropriate uncertainty paradigm for fossil power plant applications of diagnostic expert systems.