Continuous Interactive Learners for Mission Planning

Case Study


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.


Develop machine learning methods to update and optimize planning models.

    • 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.


    • 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.