Game Theoretic Approach to Threat Prediction and Situation Awareness

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1 June 2007
G. Chen, D. Shen, C. Kwan, J. Cruz, Jr., M. Kruger, E. Blasch

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The strategy of data fusion has been applied in threat prediction and situation awareness. The terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called “JDL Data-Fusion Model.” Higher levels of the model call for prediction of future development and awareness of the development of a situation. It is known that the Bayesian Network is an insightful approach to determine optimal strategies against an asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a data-fusion approach for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. Asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggregation in level-two fusion and their intents are predicted by a decentralized Markov (stochastic) game model with deception in level-three fusion. We have evaluated the feasibility of the advanced data fusion algorithm and its effectiveness through extensive simulations.