Opponent state modeling in RTS games with limited information using Markov random fields
One of the critical problems in adversarial and imperfect information domains is modeling an opponent's state from the information available to the acting agent. In the domain of real time strategy games, this information consists of the portion of the map and enemy units visible to the agent at any given point in the match. From this, we wish to infer the true values of the opponent's state, to inform both current actions and planning ahead. We present a graphical model for opponent modeling in StarCraft: Brood War that uses observed quantities to infer distributions for unseen features. We train and test this model using replays of professional play, and show that our results improve upon prior work. In addition, we present a new metric for measuring aggregate performance of a model within this domain. Finally, we consider possible use cases and extensions for this model.
