Adversarial reasoning is of the first importance for defence and security applications since it allows to (1) better anticipate future threats, and (2) be proactive in deploying effective responses. In this paper, we address the two subtasks of adversarial reasoning, namely adversarial plan recognition and strategy formulation, from a generative, game-theoretic perspective. First, a set of possible future situations is computed using a contextual action model. This projected situation serves as a basis for building a set of Markov games modeling the planning strategies of both the defender and his adversary. Finally, a library of critical plans for the attacker and a library of best responses for the defender are generated automatically by computing a Nash equilibrium in each game. The adversarial plan recognition task therefore consists of inferring a probability distribution over the set of possible plans of the adversary, while the strategy formulation problem reduces to the selection of the most appropriate response. Initial results on a urban warfare scenario suggest that our framework can be useful to model complex strategic interactions inherent to plan recognition in adversarial situations.