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Artificial intelligence and machine learning for future army applications
Author(s): John M. Fossaceca; Stuart H. Young
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Paper Abstract

Based on current trends in artificial intelligence (AI) and machine learning (ML), we provide an overview of novel algorithms intended to address Army-specific needs for increased operational tempo and autonomy for ground robots in unexplored, dynamic, cluttered, contested, and sparse data environments. This paper discusses some of the motivating factors behind US Army Research in AI and ML and provides a survey of a subset of the US Army Research Laboratory’s (ARL) Computational and Information Sciences Directorate’s (CISD) recent research in online, nonparametric learning that quickly adapts to variable underlying distributions in sparse exemplar environments, as well as a technique for unsupervised semantic scene labeling that continuously learns and adapts semantic models discovered within a data stream. We also look at a newly developed algorithm that leverages human input to help intelligent agents learn more rapidly and a novel research study working to discover foundational knowledge that is required for humans and robots to communicate via natural language. Finally we discuss a method for finding chains of reasoning with incomplete information using semantic vectors. The specific research exemplars provide approaches for overcoming the specific shortcomings of commercial AI and ML methods as well as the brittleness of current commercial techniques such that these methods can be enhanced and adapted so as to be applicable to army relevant scenarios.

Paper Details

Date Published: 4 May 2018
PDF: 18 pages
Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 1063507 (4 May 2018); doi: 10.1117/12.2307753
Show Author Affiliations
John M. Fossaceca, U.S. Army Research Lab. (United States)
Stuart H. Young, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 10635:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Michael A. Kolodny; Dietrich M. Wiegmann; Tien Pham, Editor(s)

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