| Abstract |
In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well as identifying gaps in information. This process requires the ability to reason inductively, for which we will exploit the machines' ability to 'learn' from data. However, important phenomena are often rare in occurrence, thus severely limiting the availability of instance data for training, and hence the applicability of many machine learning approaches. Therefore, we present the benefits of Subjective Logic Bayesian Networks—i.e. Bayesian Networks with imprecise probabilities—for situational understanding; and the potential role of conversational interfaces for supporting decision makers in the evolution of situational understanding. |