4 May 2018 Artificial intelligence and machine learning for future army applications
Author Affiliations +
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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John M. Fossaceca, John M. Fossaceca, Stuart H. Young, Stuart H. Young, } "Artificial intelligence and machine learning for future army applications", Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 1063507 (4 May 2018); doi: 10.1117/12.2307753; https://doi.org/10.1117/12.2307753


Hyperpyramids For Vision-Driven Navigation
Proceedings of SPIE (March 28 1988)
Toboggan contrast enhancement
Proceedings of SPIE (February 29 1992)
Locating Mushrooms For Robotic Harvesting
Proceedings of SPIE (February 28 1990)
Object segmentation for helicopter guidance
Proceedings of SPIE (February 29 1992)

Back to Top