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ADVERSARIAL INTENT INFERENCE FOR PREDICTIVE BATTLESPACE AWARENESS
AUTHOR(S)Eugene Santos, Jr.
FUNDING NUMBERSG -F30602-01-1-0595 PE -62702F PR -558B TA -II WU -09
PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)University of Connecticut Office for Sponsored Programs 438 Whitney Road, Extn, Unit 1133 Storrs CT 06269-2938
PERFORMING ORGANIZATION REPORT NUMBERN/A
SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL
12b. DISTRIBUTION CODE
ABSTRACT (Maximum 200 Words)Designed and developed a cognitive architecture to model the adversary which forms the basis for the Adversary Intent Inferencing (AII) Module. Designed, developed, and implemented the AII Module based on both Bayesian Networks and Bayesian Knowledge Bases for adversarial modeling, course of action prediction, explanation, and inference of adversary intent. All functioning in both wintel and Unix environments. Integrated AII module into prototype system for modeling and predicting the adversary based on the Battle of Khafji scenario. Integrated AII module in Force Structure Simulation wargaming system and demonstrated emergent behavior when Red force behavior is varied (AFRL/IFTC). Developed Dynamico tool for constructing Bayesian fragments and templates into libraries for use by the AII -resulted in a MS Thesis. AII transitioned into Phase I and Phase II SBIR Projects under Securboration -"Emergent Adversarial Modeling System".
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Final Project SummaryGoal: Design and develop computational framework for adversarial modeling and intent inferencing for decision support Approach: Dynamically capture and predict enemy interests, goals, rationale, and courses of action under uncertainty through machine learning and Bayesian networks To achieve adversarial intent inferencing requires the ability to (1) fuse information (observables) from sensors and intelligence sources regarding the adversary, (2) infer adversary intent and goals, and (3) predict adversary courses of action (COA). In total, adversary intent inferencing (AII) provides these three key functions while also taking into consideration a number of utility issues:• AII must be able to explain the basis of its predictions; why is the adversary pursuing a predicted goal? What is driving the adversary to pursue these COAs? Must be able to model and take into account many factors including soft factors such as political environment, personality ...
