Making Adversary Decision Modeling Tractable with Intent Inference and Information Fusion 1

Military and domestic security analysts and planners are facing threats whose asymmetric nature will sharply increase the challenges of establishing an adversary's intent. This complex environment will severely limit the capabilities of the classic doctrinal approach to diagnose adversary activity. Instead, a more dynamic approach is required - adversary decision modeling (ADM) - that, while a critical capability, poses a range of daunting technological challenges. We are developing methodologies and tools that represent a tractable approach to ADM using intelligent software-based analysis of adversarial intent. In this paper we present work being performed by our team (University of Connecticut, Lockheed Martin Advanced Technology Laboratories, and the Air Force Research Laboratory Human Effectiveness Directorate) toward a preliminary composite theory of adversary intent and its descriptive models, to provide a coherent conceptual foundation for addressing adversary decision processes, tasks, and functions. We then introduce notional computational models that, given own system- of-systems actions (movements and activities) and observations of an adversary's actions and reactions, automatically generate hypotheses about the adversary's intent. We present a preliminary software architecture that implements the model with: (1) intelligent mobile agents to rapidly and autonomously collect information, (2) information fusion technologies to generate higher-level evidence, and (3) our Intent Inference engine that models interests, preferences, and context.