Abstract
Military deception is an action executed to deliberately mislead enemy’s decision by deceiving friendly forces intention. In the lessons learned from war history, deception appears to be a critical factor in the battlefield for successful operations. As training using war-game simulation is growing more important, it is become necessary to implement military deception in war-game model. However, there is no logics or rules proven to be effective for CGF(Computer Generated Forces) to conduct deception behavior automatically. In this study, we investigate methodologies for CGF to learn and conduct military deception using Reinforcement Learning. The key idea of the research is to define a new criterion called a “deception index” which defines how agent learn the action of deception considering both their own combat objectives and deception objectives. We choose Korea Marine Corps Amphibious Demonstrations to show applicability of our methods. The study has an unique contribution as the first research that describes method of implementing deception behavior.
Publisher
The Korea Institute of Military Science and Technology