CDAKAT: Cognitive Decision Aids Knowledge Acquisition Toolset

Tools for building and populating adaptive, model-based information and decision aiding systems.

The Rotorcraft Pilot’s Associate(RPA) demonstrated the utility of Cognitive Decision Aiding Systems (CDASs). Unfortunately, the knowledge required to build CDASs is a tremendous hurdle to their development, fielding and maintenance. In Phase I, SIFT researchers documented the largest hurdle to efficient CDAS knowledge management as the ‘gap’ between Knowledge Acquisition (KA) and Software Development (SD). Technology exists to capture knowledge; putting it into a useful, accessible format for later SD lags behind. In Phase II, SIFT researchers implemented and tested the prototype solution: TAGGER. TAGGER will enable richer communication between KA and SD by allowing ‘tagging’ knowledge as it is acquired, using SD-relevant terms. Tagging will be done during knowledge elicitation and will require little extra effort beyond taking notes. Because tags are SD-relevant, later knowledge inspection is facilitated and links are carried forward even into testing and re-design. Researchers used the industry standard Unified Modeling Language (UML) to structure ‘tags’ and integrate them into SD, and illustrating UML’s ability to duplicate the Task Network Toolset used on RPA.