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6 June 2022 Multi agent AI for tactical maneuvering
Kunal Srivastava, Amit Surana
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Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130K (2022) https://doi.org/10.1117/12.2617157
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
In this paper we explore the application of recent advances in AI/algorithmic game theory for autonomous tactical maneuvering in aerial combat mission scenarios. Tactical maneuvering requires autonomous aircrafts to learn defensive/offensive tactics while reasoning about an intelligent adversary, making it a challenging decision making problem. We consider a one-vs-one aerial dogfighting scenario and formulate it as a two-person zero-sum perfect information game. To solve this game online we apply simultaneous move Monte Carlo Tree Search (MCTS) since both aircrafts simultaneously take maneuvering decisions to gain tactical advantage. Compared to other techniques, MCTS enables efficient search over long horizons and uses self-play to select best maneuver in the current state while accounting for the opponent aircraft tactics. We explore different algorithmic choices in MCTS and demonstrate the framework numerically in a simulated 2D tactical maneuvering application.
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Kunal Srivastava and Amit Surana "Multi agent AI for tactical maneuvering", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130K (6 June 2022); https://doi.org/10.1117/12.2617157
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KEYWORDS
Monte Carlo methods
Computer simulations
Artificial intelligence
Tactical intelligence
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