2022 Assessment of the DEVCOM Army Research Laboratory (2024) / Chapter Skim
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5 Competency Conclusions and Recommendations and Crosscutting Conclusions and Recommendations
Pages 63-68

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From page 63...
... Recommendation: The DEVCOM Army Research Laboratory should prioritize the maintenance of the Information for Mixed Squads (INFORMS) Laboratory and other computing resources and platforms necessary to the work of the humans in complex systems competency.
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From page 64...
... Recommendation: The humans in complex systems core competency leadership should consider conducting a review of relevant work within the humans in complex systems core competencies to determine which projects may be ready for a transition into real-world settings and real-world systems. Additionally, when appropriate to the project needs, the Army Research Laboratory may consider investing in wearable sensor technology designed for real-world settings.
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From page 65...
... Recommendation: The DEVCOM Army Research Laboratory should consider improving staff resources in game development and, in particular, art assets development, which includes access to skilled artists and designers. Conclusion: A makerspace could be designed to support the antidisciplinary, experimental, and speculative design research that the hybrid human–technology intelligence core competency team aims to undertake.
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From page 66...
... Recommendation: The DEVCOM Army Research Laboratory should consider developing a database that captures what molecules are available, what prior formulation pathways have been performed with such molecules and their result; and the resulting performance, sensitivity, and stability properties of each attempt. Conclusion: The modeling, simulation, and experimental characterization of energetics competency relies heavily on the concept of bridging scales.
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From page 67...
... Recommendation: The DEVCOM Army Research Laboratory should consider stronger connections with the researchers studying rotorcraft at NASA's Ames Research Center and associated computational model development at Ames Research Center and Stanford University. Conclusion: In the guidance and navigation of weapons systems core competency the support for extramural research is leading to high-quality innovations specifically in the areas of data-driven autonomy, robust atomic clocks, and optical time transfer.
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From page 68...
... Crosscutting Recommendation: DEVCOM Army Research Laboratory should ensure that each competency contains sufficient data science, statistics, and machine learning expertise to take advantage of experimental and simulation data, and to help develop new models for design and development.
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Key Terms

  • devcom army
  • core competency
  • data science
  • terminal effects
  • game development

  • machine learning
  • devcom arl
  • ames research
  • extramural research
  • experimental research

  • competency team
  • effects competency
  • current staff
  • arl staff
  • intelligence core

  • assets development
  • sensor technology
  • unmanned aerial
  • wearable sensor
  • extramural collaborations

  • training opportunities
  • tether design
  • aerial vehicle
  • design process
  • computational model

  • crosscutting conclusions
  • weapons sciences
  • contemporary work
  • science expertise
  • predicting humans


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