A General Context-Aware Framework for Improved Human-System Interactions

For humans and automation to collaborate and perform tasks effectively, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact more intuitively and naturally with automation that is reusable across domains by modeling context and algorithms at a higher level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work toward a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.