Continuous Interactive Learners for Mission Planning

Case Study

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.

Solution:

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.

Outcome:

    • 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.