SBIR-STTR Award

ASI proposes to develop and evaluate a Phase I Demonstration System for the Improved Fusion Algorithm System (IFAS) based on its artificial intelligence technology for building Associate Systems. This Demonstration System will be based on requirements analysis with a written requirements document and will be evaluated by military operator users. Requirements analysis will include performance of cognitive task analysis to identify and detail scenarios which can be used to structure the Phase I demonstration system. The existing ASI Solomon (registered trademark) technology for Associate Systems will be augmented with stochastic networks using CVAR distributions and statistics according to the requirements. The requirements will also be used to structure user evaluations.

Keywords:
information fusion, inference modeling, optimization, stochastic, conditional value at risk

The military’s increasingly capable net-centric environment offers clear benefits and undeniable challenges. Due to the development of new sensor platforms and sensor technologies, physics-based information capabilities have increased but limitations on human/system exploitation capabilities have limited sensor tasking such that these capabilities are underutilized. IF there were enough people – and enough time – it might be possible to make sense of the data produced by today’s sensors. Of course, neither condition holds. Given this challenge, we advocate the development of intelligent systems that help people function more effectively in spite of high data rates and short time-frames.

Keywords:
artificial intelligence, conditional value at risk, sensor fusion, uncertainty, intelligent systems, OODA loop