二对二目标防御博弈中对抗基于分配策略的异质角色
Heterogeneous Roles against Assignment Based Policies in Two vs Two Target Defense Game
AI总结：
针对二对二目标防御博弈中一对一分配分解导致次优的问题，提出攻击方队友感知的异质角色策略，给出获益充分条件并通过仿真验证其优于基于分配的防守策略。
摘要：
本文考虑一种目标防御博弈：攻击方队伍试图抵达一个高价值目标，而防守方队伍则试图通过在目标之外捕获他们来阻止这一行动。为应对维数灾难，求解此类队伍对队伍博弈的一种流行方法是将其分解为一组一对一博弈。这种近似假设被分配到不同一对一博弈中的队友之间相互独立，忽视了存在更丰富合作行为集合的可能性，最终导致次优性。本文为攻击方队伍提供了具备队友感知能力的策略，并证明在防守方仍采用基于分配的策略时，这些策略能够优于基于分配的策略。更具体地说，攻击方策略包含异质角色，其中一名攻击者主动拦截一名防守者，以帮助其队友抵达目标。我们给出了此类策略使攻击方受益的充分条件，并通过数值仿真验证了结果。
英文摘要：
In this paper, we consider a target defense game in which the attacker team seeks to reach a high-value target while the defender team seeks to prevent that by capturing them away from the target. To address the curse of dimensionality, a popular approach to solve such team-vs-team game is to decompose it into a set of one-vs-one games. Such an approximation assumes independence between teammates assigned to different one-vs-one games, ignoring the possibility of a richer set of cooperative behaviors, ultimately leading to suboptimality. In this paper, we provide teammate-aware strategies for the attacker team and show that they can outperform the assignment-based strategy, if the defenders still employ an assignment-based strategy. More specifically, the attacker strategy involves heterogeneous roles where one attacker actively intercepts a defender to help its teammate reach the target. We provide sufficient conditions under which such a strategy benefits the attackers, and we validate the results using numerical simulations.
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。
