A stochastic game-theoretic approach for analysis of multiple cooperative air combat

This paper presents a game theory-based methodology to analyze multiple beyond-visual-range (BVR) air combat scenarios. The combat scenarios are formalized as a stochastic game consisting of a sequence of normal-form games with a continuous sub-game. The proposed game formulation improves the scalability by reducing the decision space while taking advantage of some underlying symmetry structures of the combat scenario. The equilibrium strategy and the value functions of the game are computed through some dynamic programming procedure; the impact of the aircraft's velocity and the cooperation scheme for the combat is analyzed based on the equilibrium strategies.

[1]  K. Well,et al.  Pursuit-evasion in medium-range air-combat scenarios , 1987 .

[2]  Kai Virtanen,et al.  Modeling Air Combat by a Moving Horizon Influence Diagram Game , 2006 .

[3]  B. Jarmark.  A missile duel between two aircraft , 1985 .

[4]  C. Isbell,et al.  Horizon-based Value Iteration , 2007 .

[5]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[6]  J. Shinar,et al.  A simple Markov model for the analysis of multiple air combat , 1984 .

[7]  Biao Wang,et al.  Air Combat Decision-Making for Cooperative Multiple Target Attack Using Heuristic Adaptive Genetic Algorithm , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Kai Virtanen,et al.  Game Optimal Support Time of a Medium Range Air-to-Air Missile , 2006 .

[9]  W.M. McEneaney,et al.  Stochastic game approach to air operations , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Hongjun Zhang,et al.  An Improved Genetic Algorithm for Target Assignment, Optimization of Naval Fleet Air Defense , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[11]  Michael Lewis,et al.  Game theory for automated maneuvering during air-to-air combat , 1990 .

[12]  Greg L. Zacharias,et al.  Air combat tactics optimization using stochastic genetic algorithms , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[13]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.