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Knowledge-Level Distributed Active Data Platforms for Ops-Log Synchronization

Description:

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Artificial Intelligence/ Machine Learning (AI/ML)

OBJECTIVE: The purpose of this topic is to significantly extend the Army’s current effort to establish a tactical-level data platform for collecting and disseminating relevant battlefield data by improving its representation and information value to the human decision-making process and provide a foundation for autonomy and automated analytic reasoning (AI/ML) through the data mesh construct. Tactical operational forces and sustainment forces prosecuting missions in contested environments, need the ability to improve understanding, synchronization, and situational awareness. This effort will expand and improve the ongoing efforts of the data-level tactical data platform which have established a number of use cases, benefits, and process flows. The topic team intends to leverage existing partnerships and experimentation to test, evaluate, and refine the concepts and usage over this effort as well.

DESCRIPTION: This capability should fold into the ongoing PEO C3T Tactical Data Platform effort, adding technology for hybrid knowledge graph technology, richer knowledge-based representations, and facilitating more advanced decision support and reasoning capabilities utilizing AI/ML. This would be fielded as part of the C3T Tactical Data Platform across the full spectrum of tactical level operational and sustainment units, leveraging the computing infrastructure and communications channels already defined for the current Tactical Data Platform capabilities. The expectation is that companies can solve several problems with one solution; therefore, the request has identified five (5) areas of improvement: Automated Transformation of Data to Knowledge, Knowledge-Level Representation of the Tactical Situational Awareness with embedded meaning, Representing and Continuously Reasoning over Plans, Space, Time, and Entity State, Situational pattern recognition and model projection for automated detection of potentially impacting Patterns/events, and bridging Knowledge between Operations and Logistics to ensure synchronized and orchestrated awareness and unity of effort.

PHASE I: This is a Direct to Phase II topic (DP2). The commercial market for these knowledge-level automation and reasoning enhancements are at a high enough Technology Readiness Level (TRL) for this to be a Phase II topic. As part of the submission package, the proposing company will be required to include specific tangible examples of existing capabilities within each of the sub-areas they are proposing. The existing capabilities should be described with details of demonstrated capability, screenshots and references where available, and details of the companies, organizations, or events in which they were demonstrated. The company will be asked to demonstrate each of these capabilities in an Army tactical scenario or event that will occur 9 months into the Direct-to-Phase II award. The company submissions package will also need to provide specific evidence or demonstration of the technologies ability to operate in the constraints of a tactical environment, including intermittent communications, low-bandwidth, noisy data, and limited computational power.

(DIRECT TO) PHASE II: As a Direct to Phase II proposal, proposal submission should include a roadmap of the expected deliverables:

  • 3MAC: Design and Model Review
  • 6MAC: Phase I validation of Key Technologies
  • 9MAC: Build 1 Demonstration of Base Knowledge-level capabilities
  • 12MAC: Build 2 Integration with current Tactical Data Platform
  • 15MAC: Build 3 Experimentation/First Evaluation Release
  • 18MAC: Build 4 Exp Revision / Ops-Log Sync
  • 22MAC: Build 5 Final Release / Second Evaluation Release
  • 24MAC: Final Report

PHASE III DUAL USE APPLICATIONS: There is high dual-use potential for automated data platforms. They can help companies make better decisions and improve efficiency. As businesses grow, they will need the ability to manage and analyze large sets of data. These platforms will allow them to make informed decisions from the results of these analyses. Complete the maturation of the company’s technology developed in Phase II and produce prototypes to support further development and commercialization.

KEYWORDS:  AI/ML; Autonomy; Data-driven; Decision-making; Data Platforms; Knowledge; Pattern Recognition

REFERENCES:

  1. https://access.redhat.com/documentation/en-us/reference_architectures/2017/html/microservice_architecture/microservice_architecture
  2. https://www.ibm.com/cloud/architecture/architectures/aiAnalyticsArchitecture/reference-architecture/
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