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Case Studies

Continuous Interactive Learners for Mission Planning (CILEMP)

Continuous Interactive Learners for Mission Planning (CILEMP)

US Army LDAC faced the challenge of managing a complex, evolving IT environment critical to Army logistics, and Knexus delivered integrated, secure, and efficient IT support that enhanced system reliability and agility while ensuring timely access to essential logistics data.

Continuous Interactive Learners for Mission Planning (CILEMP)
PROBLEM

Current automated tools for defense mission planning rely on obsolete knowledge models to function that require manual encoding and testing, reducing the effectiveness of automated mission planning.

LDAC must provide the Army community with vital logistics data necessary for the planning, conducting and sustainment of warfighting capability worldwide.

Knexus
Solution

Develop machine learning methods to update and optimize planning models.

  • Build tool to convert 2010 data into the 2020 Census format and produce a test dataset that can be used for rapid experimentation.
  • Built and managed a Jenkins system that integrates with all Disclosure Avoidance Census code to ensure smoother production and code iteration.
  • Leveraged AWS Cloud Infrastructure to create versioned testing tools that allowed for reproducibility of tests on any current or historical version of the DAS system.
  • Brought scientific research code to production ready standards for improved performance, legibility, and flexibility.
Develop CILEMP, a collection of advanced multi-strategy machine learning software tools that acquire and update planning models for AI-based mission planning systems.
Develop CILEMP to use methods for updating planning models that utilize mission performance data and user feedback including after action reports, planning decisions, and critiques of system performance.
Customer Outcome

Customer Outcome

Continuous Interactive Learners for Mission Planning (CILEMP)
CILEMP automatically improves mission planner performance by utilizing and exploiting mission performance data and user feedback to better develop new protocols and mission plans.
Through machine learning CILEMP is able to process user feedback and after mission reports automatically without the need for manual encoding reducing processing time to seconds.

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