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Adapting to Affect and Personality 55ABAIS ADAPTIVE FRAMEWORKUSER STATE ASSESSMENTAffect Assessment- Physiological- Diagnostic tasks- Self report- KB methodsaffective stateIMPACT PREDICTIONGENERICAffectImpact RBTASKSPECIFICAffectImpact RBTask / ContextCharacteristicsBelief Assessment- KE techniques- KB methods- Diagnostic tasksindividual beliefsBeliefsImpact RBBeliefsImpact RBspecific affect/belief inducedimpact on taskperformanceDSS GUIspecific GUIadaptationdirectivesGUI ADAPTATIONSelectbest infopresentationstrategyIdentifyadditionalrequiredinformationfrom DSSUI Strategy KBtask-specificcompensatorystrategySTRATEGY SELECTIONSelect compensatorystrategiesStrategyKBTask SimulationDECISION SUPPORTSYSTEMFigure 6.1.ABAIS Affect-Adaptive Architecture.3. Adaptive Methodology and ArchitectureWe developed a methodology designed to compensate for performance biasescaused by users’ affective states and active beliefs [4]. The methodologyconsists of four stages: 1) assessing the user’s affective state and performancerelevantbeliefs; 2) identifying their potential impact on performance (e.g., focuson threatening stimuli); 3) selecting a compensatory strategy (e.g., presentationof additional information to reduce ambiguity); and 4) implementingthis strategy in terms of specific GUI adaptations (e.g., presenting additionalinformation, or changing information format to enhance situation awareness).This methodology was implemented within an architecture: the Affect andBelief Adaptive Interface System (ABAIS). The ABAIS architecture consistsof four modules, described below, each implementing the corresponding stepof the adaptive methodology (see Figure 6.1): User State Assessment, ImpactPrediction, Strategy Selection, and GUI Adaptation.