In multi-agent environments, the task of agent tracking (i.e., tracking other agents’ mental states) increases in difficulty when a tracker (tracking agent) only has an imperfect model of the trackee (tracked agent). Such model imperfections arise in many realworld situations, where a tracker faces resource constraints and imperfect information, and the trackees themselves modify their behaviors dynamically. While such model imperfections are unavoidable, a tracker must nonetheless attempt to be adaptive in its agent tracking. In this paper, we analyze some key issues in adaptive agent tracking, and describe an initial approach based on discrimination-based learning. The main idea is to identify the deficiency of a model based on prediction failures, and revise the model by using features that are critical in discriminating successful and failed episodes. Our preliminary experiments in simulated air-to-air combat environments have shown some promising results but many problems remain open for future research.