We introduce a model for multi-agent interaction problems to understand how a heterogeneous team of agents should organize its resources to tackle a heterogeneous team of attackers. This model is inspired by how the human immune system tackles a diverse set of pathogens. The key property of this model is a "cross-reactivity" kernel which enables a particular defender type to respond strongly to some attacker types but weakly to a few different types of attackers. We show how due to such cross-reactivity, the defender team can optimally counteract a heterogeneous attacker team using very few types of defender agents, and thereby minimize its resources. We study this model in different settings to characterize a set of guiding principles for control problems with heterogeneous teams of agents, e.g., sensitivity of the harm to sub-optimal defender distributions, and competition between defenders gives near-optimal behavior using decentralized computation of the control. We also compare this model with existing approaches including reinforcement-learned policies, perimeter defense, and coverage control.

翻译：本文引入了一个面向多智能体交互问题的模型，旨在理解异构智能体团队应如何组织其资源以应对异构攻击者团队。该模型的灵感来源于人体免疫系统如何应对多样化病原体。其核心特性在于一个“交叉反应”核函数，该核函数使得特定防御者类型能强烈响应某些攻击者类型，而对少数其他攻击者类型反应较弱。我们表明，借助这种交叉反应，防御者团队能够通过极少数类型的防御智能体以最优方式对抗异构攻击者团队，从而最小化资源消耗。我们在不同情境下对该模型进行研究，以刻画面向异构智能体团队控制问题的一系列指导性原则，例如次优防御者分布对危害的敏感性，以及防御者之间的竞争如何通过控制问题的分散式计算产生接近最优的行为。此外，我们还将该模型与现有方法（包括基于强化学习的策略、边界防御和覆盖控制）进行了比较。