Learning Combat Outcomes in Creative Assembly’s Total War

Abstract

This paper outlines a supervised learning approach to model the combat outcomes of 1 vs 1 unit match-ups in strategy games. This is a core system in Creative Assembly's Total War. It is used to calculate combat outcomes in the auto-resolver and informs decisions of the battle AI whether units should engage or retreat from combat. We propose an alternative to the heuristic rules-based approach by using an automated framework to learn the combat outcomes. We describe our experiments, challenges and demonstrate that our model can accurately predict the unit's combat outcomes.