Computer Architectures for Understanding: CBNX

Situation awareness involves the real-time understanding of emerging events, impacts, threats, and effective responses. Situation awareness requires the mutual interplay of perception, comprehension, and anticipation to dynamically refocus attention, adapting sensor and cognitive resources to accurately track unfolding events. The objective of this research is to continue development of a computational architecture for autonomous situation awareness for deployment in distributed sensor network applications. This architecture will provide real-time assessment of events, impacts and threats for complex phenomena such as weather systems, forest fires, ground or sea traffic, force movements, etc. A Phase I prototype system for Weather Situation Awareness was developed. Phase I research illustrated the impact of fuller context awareness on the performance level of automated weather data classification and prediction algorithms. The Phase I effort also demonstrated the potential of situation awareness to enhance standard machine learning algorithms through the application of context aware learning strategies. The Phase II effort will initially continue the Phase I focus on Weather Situation Awareness but will elaborate and generalize the features and components of the situation awareness architecture, allowing it to port rapidly to new situation awareness domains, including tracking and analysis of vehicle traffic patterns, forest fires, and force movements.