Epistemic decisions about which sources to trust and query are critical for a decision-maker, when the end-goal decisions are to be made using limited resources. Toward this, we previously proposed preliminary extensions to classical measures of Value of Information (VoI) for imprecise belief states represented by belief functions relying on a general observation model. These methods primarily aim to assist a decision-maker toward making rational decisions, by generating necessary metadata about the information that is being considered for the decision-making task (e.g. probability of source reliability, degree of self-confidence expressed by the source). In this paper, we explore the behavior and performance of our previously proposed belief theoretic VoI measures, the Evidential Expecetd Value of Sampled Information (EEVSI), and propose a procedure to sequentially select sources to query. We also consider the case where the information sources are providing contradictory evidence. We leverage a maritime surveillance scenario, where the decision-maker has to make a rational ordered selection of information sources among a set of both physical sensors and human sources, to illustrate the behavior of the proposed method. We compare the proposed policy with a traditional probability-based approach in a simulation environment. We conclude by providing some insights on future research directions to further expand on our proposed new measures.

One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.

Theory of belief functions (Dempster-Shafer theory) is one of the most commonly used mathematical frameworks in the field of uncertain information representation. Two important areas of research in its context are that of evidence combination and decision making. Although they are often considered theoretically separate, the combination process itself drives the final decision. The information contained in each of the sources propagates through the belief aggregation process, and impacts the final assessment to some degree. If the decision made has an impact on the real-world; particularly in scenarios where a wrong decision may bring about significant risks it is prudent to be able to identify the key drivers of this assessment. In this paper we present a novel method of identifying the relative contribution of each source of evidence to the final belief, thus making it possible to track provenance across the information fusion process. This is likely to be useful in intelligent decision-support systems utilising belief functions, where lack of transparency may be hindering adoption. Unlike traditional methods, which focus on the content of the contributing source only, the approach proposed here is based on analysis of dissimilarity between the contributing sources; the result of the fusion process and the decision made. The behaviour of this metric is analysed through simulation. It is shown that the proposed measure performs well with regard to identifying the source having the most significant impact on the decision, often outperforming more traditional metrics.