Some challenges associated with the classical Differential Game Theory approach are explored herein.
When the equilibrium control policies can be obtained analytically, they are suitable for onboard / real-time implementation. Differential Game Theory provides the means to obtain a rigorous solution to relevant scenarios in the form of saddle-point equilibrium control policies and the min/max (or max/min) cost / reward in the case of zero-sum games. However, the performance of these controllers compared to the optimal (or equilibrium) policies is generally unknown. Machine learning approaches such as reinforcement learning and neural networks have been demonstrated to be capable of developing controllers for large teams of agents.
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Skirmish-level tactics are important because they can provide insight into how to approach larger scale conflicts (many vs. In most cases, the tactics considered are comprised of the control policy of the agents (i.e., their spatial maneuvers), but may also include role assignment (e.g, whether to act as a decoy or striker) as well as discrete decisions (e.g., whether to engage or retreat). The problem areas include pursuit-evasion and target guarding, either of which may be considered as sub-problems within military missions such as air-to-air combat, suppression/defense of ground-based assets, etc. two - as well as the fact that the goal or objective of each team is well-established. Skirmish-level refers to both the size of the adversarial engagement - generally one vs. This document outlines a research plan focused on skirmish-level tactics to militarily relevant scenarios. Supremacy in armed conflict comes not merely from superiority in capability or numbers but from how assets are used, down to the maneuvers of individual vehicles and munitions.
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