Possibilistic vs Evidential Valuation Algebra Networks

Realistic reasoning applications typically involve many interrelated variables and require the interpretation of data that is both heterogeneous in nature and affected by various types of uncertainty. Accordingly, in this paper we investigate the performance of valuation based algebra networks for reasoning in uncertain multivariate systems. Specifically, we consider networks built from two different approaches to modelling uncertainty: possibility theory and Dempster-Shafer evidence theory. To compare these differing networks, we propose a new possibilistic counterpart to the uncertain implication rule that exists in evidential networks. Using the Captain's decision problem, we analyse the performance of these networks when estimating the number of days a ship will be delayed based on a mixture of uncertain knowledge. We demonstrate that the evidential network is more cautious to changes in uncertainty whereas the possibilistic network is more sensitive. This characteristic could allow the possibilistic network to be used to perform sensitivity analysis on a system.