利用最优控制建模大规模对抗性蜂群作战
Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control
AI总结：
本文提出利用最优控制建模大规模对抗性蜂群作战，通过考虑agent损耗和空间动态，有效捕捉对抗互动的动力学。
摘要：
我们研究了在显式对抗条件下大规模自主系统最优控制的问题，纳入了随时间推移agents的随机破坏。在许多此类系统中，对抗性互动表现为不同的agents或群体相互竞争。现有理论和建模框架中一个关键但常被忽视的因素是agents在操作期间的随机损耗。因此，有效的建模和控制策略必须考虑agent损耗和空间动态。鉴于agent生存的本质上随机性，直接解决这个问题具有挑战性。为此，我们提出并评估了三种近似数值建模方法，其中agent的生存概率随时间推移根据其相对位置确定性地减少。我们将这些方案应用于一个场景，其中agents保卫一个高价值单位以对抗攻击蜂群。我们的结果表明，只要损耗和空间定位紧密整合，这些模型可以有效捕捉此类互动的动力学。这些发现对于广泛存在的对抗自主性场景具有相关性，其中agent的位置和生存概率都起着关键作用。
英文摘要：
We study optimal control of large-scale autonomous systems under adversarial conditions in which agents may be probabilistically destroyed during an engagement. Because attrition changes the population that generates the spatial interaction dynamics, treating survival and motion independently can produce physically inconsistent solutions. We formulate a stochastic variable-population benchmark and examine three deterministic approximations that propagate agent survival probabilities from relative geometry. The models are applied to defense of a high-value unit against an attacking swarm and solved using direct optimal-control methods. Monte Carlo realizations of the stochastic benchmark show that deterministic models can reproduce engagement outcomes when attrition is coupled to spatial influence, whereas a decoupled formulation can exploit effectively destroyed defenders that continue to repel attackers, a failure mode we term ``ghost herding.'' Large-scale simulations and defender-number sweeps further show that this modeling choice can substantially alter predicted high-value-unit survival and inferred defensive resource requirements. The results provide a tractable framework for attrition-aware trajectory optimization and resource--survival analysis in adversarial autonomous systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
