Adversarial training aims to defend against *adversaries*: malicious opponents whose sole aim is to harm predictive performance in any way possible - a rather harsh perspective, which we assert results in unnecessarily conservative models. Instead, we propose to model opponents as simply pursuing their own goals, rather than working directly against the classifier. Employing tools from strategic modeling, our approach uses knowledge or beliefs regarding the opponent's possible incentives as inductive bias for learning. Our method of *strategic training* is designed to defend against opponents within an *incentive uncertainty set*: this resorts to adversarial learning when the set is maximal, but offers potential gains when it can be appropriately reduced. We conduct a series of experiments that show how even mild knowledge regarding the adversary's incentives can be useful, and that the degree of potential gains depends on how incentives relate to the structure of the learning task.

通过战略建模，我们的研究提出使用对手的动机作为归纳偏差学习的一种方式，通过战略训练在不确定奖励条件下防御对手，此方法甚至对对手动机的轻微了解也能有用，潜在收益程度取决于动机与学习任务结构的关系。

具有动机的对手：对抗鲁棒性的战略性替代方案