Valuation-based system

Abstract: The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. Hence the resulting network for reasoning is referred to as an evidential network. Local computations in the evidential network are carried out by inward propagation on the underlying joint binary tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the joint binary tree.

Abstract: Valuation-based systems (VBS) provide a general framework for representing knowledge and drawing inferences under uncertainty. Recent studies have shown that the VBS can also represent and solve Bayesian decision problems. This paper proposes a decision calculus for belief function theory in the VBS. The proposed calculus uses a parameter whose role is the probabilistic interpretation of an assumption that disambiguates decision problems represented with belief functions. We show that the decision problems can be solved by using local computations with the presented calculus if they are represented in the VBS properly. We also show that the presented calculus can be reduced to the one for Bayesian decision problems when probabilities, instead of belief functions, are given.