Abais: Affect and Belief Adaptive Interface System

Abstract

We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users' affective states and active beliefs. The ABAIS 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 compensatory strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for exploring a variety of user assessment methods (e.g., knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g., content- and format-based). The ABAIS performance bias prediction is based on empirical findings from emotion research, and knowledge of specific task requirements. The initial ABAIS prototype is demonstrated in the context of an Air Force combat task, uses a knowledge-based approach to assess the pilot's anxiety level, and modifies selected cockpit instrument displays in response to detected increases in anxiety levels.