Integration of deterministic inference with formal synthesis for control under uncertainty

In this work, we consider an agent playing a turn-based game in a known environment against an adversary with unknown dynamics. The model of the adversary is assumed to belong to a subclass of regular languages that can be learned in the limit. We use tools from formal methods to synthesize a control strategy for the agent to win the game as it learns the model of its adversary, if a winning strategy exists. The strategy is updated as new information about the adversary is learned. The proposed framework is tested in simulation.