SBIR-STTR Award

The need for a rational strategy to manage the evolution of user interfaces (UI) during block upgrades in complex tactical weapon systems has been a daunting, and costly, challenge for all services, especially the Navy. The emergence of open and Service-Oriented Architectures has exacerbated this problem. Vendors of these systems and the government agree that design tools are needed which provide UI designers with an environment that semi- or automatically manages these changes. Working with our Phase III transition partner, Raytheon IDS, we plan to design, development, implement, and test TRUIDTM, Toolset for Rational User Interface Design. Developed as a Java plug-in, TRUID consists of a query-based design pattern recognizer, a system analyzer for parsing the UI into chunks, a task decomposer that breaks down system tasks hierarchically, an IF-THEN UI design rule engine that optimizes widget/controller configuration given task and mission context, a document compiler that automatically reconfigures as design changes are made, and a scenario-based training aid for system and UI designers. TRUID will be prototyped in Phase II and field tested in the context of a Navy weapon system of interest, such as the CCS MK2 Weapons Control System or the AN/BYG-1 Advanced Submarine Combat System.

Keywords:
User Interface, Service-Oriented Architecture, Virginia Class Submarines, User Interface Design, Total Ship Computing Environment, Submarine Combat We

During Phase I of this SBIR effort, Anacapa Sciences, Inc. invented a new technology for analyzing and optimizing the design of critical aspects of the User Interfaces (UIs) for real-time systems that have high urgency for errorless operator performance. The technology is based on machine-learning algorithms currently used in commercial data mining applications. We discovered that this class of algorithms – a form of artificial intelligence – could be employed to analyze UI design data from historical and currently-deployed systems, to develop a mathematical knowledgebase. This knowledgebase comprises equations that quantify the successes and failures of UI designs in their support of critical aspects of user performance. Information in these equations can be used to prescribe new [or upgraded] UI design features and functions that will optimally support such critical performance.

Keywords:
Ui Design, Ui Design, Sensor Systems, User Interfaces, Machine-Learning, Applied Ai, Hci Design, Cognitive Engineering, Combat Systems