Contextual Systems Laboratory

Performing analytical and decision-making tasks in time-pressured and life-threatening operational situations has become more difficult for humans in today's increasingly complex data environment. New sensors collect data with greater dynamic range, spatial reach, sample rate and temporal history but are not temporally or spatially matched, creating a complex data environment. Computer support to help humans turn data into useful information is needed. Traditional interfaces with standard decision support software based on data filtering and graphical user interfaces are not sufficient. True human-machine collaborative environments, where both perceptual and cognitive processing is distributed between humans and machines, are required to meet today's challenges. We believe the explicit creation, maintenance, and use of context is central to the systems ability to meet the demands of a highly dynamic environments.

To meet these challenges, we are developing techniques and software that address situation-dependent information extraction, large-scale data management, and human-system integration and collaboration by performing research and development in:

• robust knowledge management, correlation and fusion techniques focused on diverse information sources,
• entity relationship and pattern recognition analysis to permit conclusions on contextual relationships,
• extraction of meaning and context from complex and cluttered data streams - leading to faster and more complete understanding of options and resulting in better decisions
• tools that provide rapid, contextually relevant effects,
• new interaction modalities between humans and systems with transparent and intuitive interface capabilities supported by context-sensitive cues to enable reach-back knowledge management and collaboration,
• understanding human-complex system interaction impact on overall system performance, with emphasis on human cognitive performance and neuroergonomics, and human knowledge representation for operations, focusing on algorithmic approaches with operator insight into models that are context-dependent.
• federated, scalable frameworks to link disparate information sources,

• Situation Understanding
• User-Centered Interfaces

Situation Understanding

Modern military operations require situation understanding by humans, robots, and other automated systems. The prevalence of mechanical and human information sources provides a wealth of data, but it is situation assessment processing, including information fusion, inferential logic, and contextual reasoning, that transform this data into actionable knowledge.

User-Centered Interfaces (UCI) optimizes human system performance through advances in Human Computer Interaction created through dynamic interactive interface design and engineering. We concentrate on the development of user centered interface technologies in three focus areas: Advanced Interaction Paradigms, Context-Intelligent Computing and Human-System Integration (HSI) Design Methods & Evaluation.