A decision support model for uncertainty reasoning in safety and security tasks

Performing safety and security tasks requires the continuous gathering and interpretation of information about objects to detect and predict events of interest. Especially, reasoning about objects' identities and intentions is crucial, but requires making use of heterogeneous information inherent with uncertainty. This makes such tasks very challenging for human operators and decision support systems are clearly needed. In this paper, we present a generic probabilistic logic model for the systematic representation and uncertainty reasoning about the identity and intentions of monitored objects. We apply the model to the area of maritime safety and security after incorporating domain specific knowledge. Experiments with simulated and realworld vessel data demonstrate the model's capabilities to draw conclusions about objects using uncertain information. The firstorder probabilistic logic we use shows to be a very powerful tool to deal with dynamic amounts of information and objects. To our knowledge it is one of the few real-world applications of probabilistic logic.