A key aspect of social interaction is the ability to exhibit and recognize variations in behavior due to different agective states and personalities. To enhance their believability and realism, socially intelligent agent architectures must be capable of modeling and generating behavior variations due to distinct affective states and personality traits on the one hand, and to recognize and adapt to such variations in the human user / collaborator on the other. In this paper we describe an adaptive user interface capable of recognizing and adapting to the user’s affective and belief state (e.g., heightened level of anxiety). The Affect and Belief Adaptive Interface System (ABMS) designed to compensate for performance biases caused by users’ affective states and active beliefs. The performance bias prediction is based on empirical findings from emotion research, and knowledge of specific task requirements. The ABMS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensator)' strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for exploring a variety of user affect assessment methods (e.g., knowledgebased, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g., content- and format-based). ABAIS prototype was implemented and demonstrated in the context of an Air Force combat task, using a knowledge-based approach to assess and adapt to the pilot’s arLxiety level.