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Jeffrey Basilotta

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Proceedings Article | 6 June 2022 Poster + Presentation + Paper

Enhanced tactical inferencing (ETI): an initial assessment

Adrienne Raglin, John Richardson, Jeffrey Basilotta, Mark Mittrick, Mathew Maness, Justine Caylor, Somiya Metu

Proceedings Volume 12113, 1211326 (2022) https://doi.org/10.1117/12.2622321

KEYWORDS: Intelligence systems, Data processing, Data modeling, Knowledge management, Analytical research, Taxonomy, Systems modeling, Visualization, Reconnaissance, Control systems

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The US Army has existing challenges associated with command and control and the execution of the operations processes, data processing, information management, and knowledge management. Continued limitations in the capability to combine and reason across explicit and tacit knowledge, due to the increased flow of data from multiple domains, is one shortfall associated with these challenges. We have created a framework to ingest various modalities of data enabling reasoning particularly for decision making tasks. The Enhanced Tactical Inferencing (ETI) framework is designed to have components that send and receive data from different information systems and sources to a reasoning module. The reasoning module is composed of sub-modules with different reasoning model profiles and functions. These sub-modules can work independently or interdependently. The output is a series of recommendations for decision making. One key model is for Uncertainty of Information (UoI). This model incorporates a series of rules and algorithms to associate uncertainty across the multiple data sources. The intent of the ETI framework is to provide recommendations to humans, intelligent systems, and combinations of both. This paper will present the details of the ETI framework, focusing on the UoI model, as well as potential refinements and applications.

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