A Multi-Model Approach to Modeling Intelligent Combat Behaviors for Wargaming

Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate the development of robust artificial intelligence (AI) for wargaming. In this study, we contribute to the research of AI methods to develop intelligent agent behaviors in combat simulations by investigating the effectiveness of a multimodel approach as compared to a single-model approach. To accomplish this, we first develop a multi-model framework that leverages supervised learning, reinforcement learning, and scripted behavior models. We then design and conduct an experiment where we compare the performance of each one of eight individual behavior models to four multi-models composed of varying numbers of these individual behavior models. Using a combat simulation with a random scenario generator, we find that a multi-model approach improved the mean score by 62.6% over the mean game score of the best-performing single-model. Additionally, we find that a multi-model with more embedded behavior models outperforms a multi-model with fewer behavior models. The outcomes of this study contribute to the broader research endeavors aimed at expanding AI capabilities to handle the intricate and expansive state-spaces characteristic of combat modeling and simulation.