The paper develops a valuation network for sequential assessment of threat under epistemic uncertainty based on theoretical foundations and semantics of imprecise probability theory. The valuations are expressed as credal sets defined by coherent probability intervals on singletons. The combination rule is the generalized Bayes rule introduced by Walley. The model of a single-target threat is based on the classical “capability-intent” paradigm in an air surveillance context. Numerical results illustrate the performance of developed credal valuation network (with imprecise probabilities) against the valuation network with precise probabilistic models.

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent? How likely is dependence/independence to hold? How high is the probability that two mixed-type variables are more than just weakly dependent? We theoretically show the properties of the approach, as well as algorithms for fast computation with it. We empirically demonstrate the effectiveness of the proposed method by analysing its performance and by comparing it with other frequentist and Bayesian approaches on a range of datasets and tasks with mixed-type variables.

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.