Adaptive Human Behavior Modeling for Air Combat Simulation

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Military simulations, especially those for personnel training and equipment effectiveness analysis, require proper human behavior models (HBMs) to play blue or red. Traditionally, the HBMs are controlled through rule based scripts. However, the doctrine-driven behavior is rigid and predictable, and more often than not unable to adapt to new situations. In most cases, the subject matter experts (SMEs) review, re-design a large amount of HBM scripts for new scenarios or training tasks, which is challenging and time-consuming. Therefore, a study of using Grammatical Evolution (GE) to generate adaptive HBMs for air combat simulation is conducted in this work. Expert knowledge is encoded with modular behavior trees (BTs) for the compatibility with the operators in genetic algorithm (GA). GE maps HBMs represented with BTs to binary strings, and uses GA to evolve HBMs with the performance fed back from simulation. Beyond visual range air combat experiments between adaptive HBMs and none-adaptive baseline HBMs are conducted to study the evolutionary process. The experimental results show that the GE is an efficient framework to generate adaptive HBMs in BTs formalism and evolve them with GA.