The State of the Art and the State of the Practice

A game theoretic approach to threat intent inference In the adversarial military environment, it is important to efficiently and promptly predict the enemy’s tactical intent from the lower level spatial and temporal information. In this paper, we propose a decentralized Markov game (MG) theoretic approach to estimate the belief of each possible enemy COA (ECOA), which is utilized to model the adversary intents. It has the following advantages: 1) Decentralized. Each cluster or team makes decisions mostly based on the local information. We put more autonomies in each group allowing for more flexibilities; 2) Markov Decision Process (MDP) can effectively model the uncertainties in the noisy military environment; 3) Game model with three players: red force (enemies), blue force (friendly forces), and white force (neutral objects); 4) Deception. With the consideration that asymmetric enemy may manipulate the information available to friendly force; we integrate the deception concept in our game approach to model the action of purposely rendering partial information to increase the payoffs of the enemy. A simulation software package has been developed with connectivity to the Boeing OEP (Open Experimental Platform) to demonstrate the performance of our proposed algorithms. Simulations have verified that our proposed algorithms are scalable, stable, and satisfactory in performance.