Near-Nash targeting strategies for heterogeneous teams of autonomous combat vehicles

Military strategists are currently seeking methodologies to control large numbers of autonomous assets. Automated planners based upon the Nash equilibrium concept in non-zero sum games are one option. Because such planners inherently consider possible adversarial actions, assets are able to adapt to, and to some extent predict, potential enemy actions. However, these planners must function properly both in cases in which a pure Nash strategy does not exist and in scenarios possessing multiple Nash equilibria. Another issue that needs to be overcome is the scalability of the Nash equilibrium. That is, as the dimensionality of the problem increases, the Nash strategies become unfeasible to compute using traditional methodologies. In this paper we introduce the concept of near-Nash strategies as a mechanism to overcome these difficulties. We then illustrate this concept by deriving the near-Nash strategies and using these strategies as the basis for an intelligent battle plan for heterogeneous teams of autonomous combat air vehicles in the Multi-Team Dynamic Weapon Target Assignment model.

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