An overview of the U.S. Army Research Laboratory's Sensor Information Testbed for Collaborative Research Environment (SITCORE) and Automated Online Data Repository (AODR) capabilities

Ward, Dennis W.; Bennett, Kelly W.

doi:10.1117/12.2267309

SPIE Proceedings

2017

DOI: 10.1117/12.2267309

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An overview of the U.S. Army Research Laboratory's Sensor Information Testbed for Collaborative Research Environment (SITCORE) and Automated Online Data Repository (AODR) capabilities

Dennis W. Ward

Kelly W. Bennett

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“…SITCORE combined with AODR provides a comprehensive end-to-end algorithm development, experimentation, demonstration, and validation platform[23].…”

mentioning

confidence: 99%

Artificial intelligence and machine learning for future army applications

Fossaceca

Young

2018

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX

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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.

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“…SITCORE combined with AODR provides a comprehensive end-to-end algorithm development, experimentation, demonstration, and validation platform[23].…”

mentioning

confidence: 99%

Artificial intelligence and machine learning for future army applications

Fossaceca

Young

2018

Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX

View full text Add to dashboard Cite

show abstract

Application of Trust Assessment Techniques to IoBT Systems

Agadakos

Ciocarlie

Copos

et al. 2019

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)

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An overview of the U.S. Army Research Laboratory's Sensor Information Testbed for Collaborative Research Environment (SITCORE) and Automated Online Data Repository (AODR) capabilities

Cited by 2 publications

References 0 publications

Artificial intelligence and machine learning for future army applications

Artificial intelligence and machine learning for future army applications

Application of Trust Assessment Techniques to IoBT Systems

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