A Frigate Movement Survival Agent-Based Approach

The position of a frigate to face some threats can augment its survival chances and therefore it is important to investigate this aspect in order to determine how a frigate can position itself during an attack. To achieve that, we propose a first method based on the Bayesian movement, performed by a learning agent, which determines the optimal positioning of the frigate by dividing the defense area into six sectors for weapon engagement and then, it makes efficient use of all the weapons available by using the sectors. The second method that we propose is called Radar Cross-Section Reduction (RCSR) movement and, it aims at reducing the exposed surface of the frigate to incoming threats before their locking phase is over. Preliminary results on these two methods are presented and discussed. Finally, an implementation of a meta-level agent which would make efficient use of both complementary methods is suggested.