Threat assessment is a high-level information fusion task which supports a decision maker in achieving a proper level of situational awareness. In this paper, we propose an approach to threat assessment that fuses both game-theoretic behavior modeling and adversarial intention recognition. In adversarial situations, where two opposing forces are competing to achieve conflicting goals in a shared environment, the behavior of the observed adversary will not only depend on the goal it is trying to achieve, but also on the decisions of its opponent. We model the planning process of such a goal-directed adversary as an Attack Stochastic Game and show how we can infer its intention by inverting this model. We evaluate the potential of our approach for early and accurate intention recognition on an illustrative scenario where a decision maker has to defend a set of valuable assets against an attacker, and we discuss planned extensions.
['Nicolas Le Guillarme', 'Abdel-Illah Mouaddib', 'Sylvain Gatepaille', 'Amandine Bellenger']
Adversarial Intention Recognition as Inverse Game-Theoretic Planning for Threat Assessment