How a machine can understand the command intent
van der Kaaij, M.
With recent technological advances, commanders request the support of artificial intelligence (AI)-enabled systems during mission planning. Future AI systems may test a wide range of courses of action (COAs) and use a simulator to test each COA’s effectiveness in a war game. The COA’s effectiveness is however dependent on the commanders’ intent. The question arises to what degree a machine can understand the commanders’ intent? Currently, the intent has to be programmed manually, costing valuable time. Therefore, we tested whether a tool can understand a freely written intent so that a commander can work with an AI system with minimal effort. The work consisted of letting a tool understand the language and grammar of the commander to find relevant information in the intent; creating a (visual) representation of the intent to the commander (back brief); and creating an intent-based computable measure of effectiveness. We proposed a novel quantitative evaluation metric for understanding the commanders’ intent and tested the results qualitatively with platoon commanders of the 11th Airmobile Brigade. They were positively surprised with the level of understanding and appreciated the validation feedback. The computable measure of effectiveness is the first step toward bridging the gap between the command intent and machine learning for military mission planning.
To reference this document use:
Course of action development
Natural language processing
Measure of effectiveness,
Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 1-18