Continuous Interactive Learners for Mission Planning (CILEMP) Navy SBIR FY2018.1 Sol No.: Navy SBIR FY2018.1 Topic No.: N181-079 Topic Title: Continuous Interactive Learners for Mission Planning (CILEMP) Proposal No.: N181-079-0430 Firm: Knexus Research Corp. 174 Waterfront Street, Suite 310 National Harbor, Maryland 22153 Contact: Kalyan Gupta Phone: (703) 321-6740 Web Site: http://www.knexusresearch.com Abstract: A key problem with automated mission planning software for military commanders is that their underlying domain models may be incomplete, imprecise, inaccurate, or inefficient and may rapidly become obsolete because of evolving domains. These issues can adversely impact the quality and the effectiveness of support offered by automated software. However, manual maintenance of models planning domain is slow, expensive and error prone. Recent advances in machine learning (ML) present an opportunity to address this problem by automatically acquiring, enriching and updating asset performance models and tactical planning knowledge for use by automated mission software. We will develop CILEMP, a software system comprising Continuous Interactive Learners for Mission Planning. CILEMP shall utilize a family of numeric and symbolic learning approaches to learn from subject matter experts(SMEs) inputs they provide during practice planning sessions and during real operations. We will investigate ML approaches such as learning from demonstration and learning from experience to acquire and update model parameters, planning preferences, and higher order structures for tactics to improve model coverage, precision, accuracy and efficiency. We will work with SMEs to develop scenarios for evaluating the feasibility of CILEMP. We will develop a software architecture specification and prototype implementation plan for Phase II. Benefits: Acquisition and maintenance of domain models for automated mission planning is difficult, slow, and expensive. This problem is even more acute in the context of rapid changes in the domain such as those resulting from introduction of unmanned vehicles (UxVs). CILEMP, when fully developed and integrated with the automated planning systems, will greatly simplify this task and reduce the total cost of ownership. With its ability to learn interactively from practice and training sessions it could reduce or even eliminate the modeling bottleneck and provide a viable approach for preserving experiential knowledge that would otherwise be lost with retiring veterans. Return