Summaries - Office of Research & Innovation
Research Summaries
Back Provisioning of a multi-int fusion environment in the era of AI and Machine Learning
Fiscal Year | 2019 |
Division | Research & Sponsored Programs |
Department | NPS Naval Research Program |
Investigator(s) | Godin, Arkady A. |
Sponsor | NPS Naval Research Program (Navy) |
Summary |
The era of AI and Machine Learning / Deep Learning (ML/DL) has a significant effect on TCPED. Dissemination architecture should support an easy integration providing decision makers (at any of the tiers including the tactical edge with its D-DIL connectivity constraints) to do more than visual exposure to the valuable information. The new era dissemination goal is to enable decision-makers with AI and ML/DL capabilities in decentralized environment. For instance, METOC SME should have tools capable to fuse organic hard-to-access METOC data sources in-situ without a need to disseminate such data to the higher tier for integration/fusion AI reasoning and ML/DL training model & analysis. Research Methodology/Plan will: • Identify distributed storage for canonical representation of all post-interpretation data modalities; NPS research views sparse-dense storage as right for integration/fusion cell for dissemination • Design ontological knowledge representation facilitating AI-based integration and multi-int fusion • Explore integration and multi-int fusion approach based on canonical array-based distributed storage • Identify distributed computing framework to execute AI-based multi-int fusion • Propose dissemination approach to concurrently support: client-based visualization/navigation and client reconstruction of AI reasoning environment from client array storage • Propose an approach to transition from Forecaster-In-The-Loop to Forecaster-On-The-Loop decisions where automation of mission-specific METOC guidance used in decision analytics is accomplished via AI and ML/DL techniques Deliverables include: report on distributed storage with canonical capabilities to model data necessary for multi-int fusion; describe the role of ontologies, if any, for multi-int fusion and multi-int fusion provisioning; report on AI/ML/DL computational frameworks; describe proposed dissemination approach which enables AI reasoning on remote devices. |
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Publications | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |
Data | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |