让多智能体网络自动调整通信结构，在效率和效果间取得平衡。
Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design
- 通过交替优化协调与网络结构，实现自配置通信拓扑。
- 在稀疏网络下决策速度比现有方法快一个数量级。
- 适用于大规模分布式协同任务，如环境监测与事件检测。
我们提出首个严格意义上的方法，使多智能体网络能够自适应调整通信拓扑，以在可扩展性与最优性之间取得平衡，用于多智能体规划。面对未来无处不在的协同自主场景——大量分布式智能体通过点对点通信执行交通监控、事件检测和环境探索等复杂任务——当前大规模网络中信息爆炸导致现有近优协调算法的计算与通信开销过大，难以实时决策。为此，我们提出交替协调与网络设计算法（Anaconda），一种可扩展且具备近优性保证的算法。在带宽约束下，该算法使各智能体优化本地通信邻域，以最大化整个网络的动作协调近似性能。相比现有方法，Anaconda是一种任意时间自配置算法，能为任意网络类型（从完全断连到完全集中）提供子优性保证，并在稀疏网络中实现决策速度提升一个数量级。为开发该算法，我们量化了去中心化带来的子优性代价，即通信最少的分布式协调成本，并借鉴多臂赌博机与基数约束下的子模最大化理论工具。我们在区域监测的模拟场景中验证了Anaconda，结果优于当前最先进算法。
原文摘要 · Abstract (English)
We introduce the first, to our knowledge, rigorous approach that enables multi-agent networks to self-configure their communication topology to balance the trade-off between scalability and optimality during multi-agent planning. We are motivated by the future of ubiquitous collaborative autonomy where numerous distributed agents will be coordinating via agent-to-agent communication to execute complex tasks such as traffic monitoring, event detection, and environmental exploration. But the explosion of information in such large-scale networks currently curtails their deployment due to impractical decision times induced by the computational and communication requirements of the existing near-optimal coordination algorithms. To overcome this challenge, we present the AlterNAting COordination and Network-Design Algorithm (Anaconda), a scalable algorithm that also enjoys near-optimality guarantees. Subject to the agents' bandwidth constraints, Anaconda enables the agents to optimize their local communication neighborhoods such that the action-coordination approximation performance of the network is maximized. Compared to the state of the art, Anaconda is an anytime self-configurable algorithm that quantifies its suboptimality guarantee for any type of network, from fully disconnected to fully centralized, and that, for sparse networks, is one order faster in terms of decision speed. To develop the algorithm, we quantify the suboptimality cost due to decentralization, i.e., due to communication-minimal distributed coordination. We also employ tools inspired by the literature on multi-armed bandits and submodular maximization subject to cardinality constraints. We demonstrate Anaconda in simulated scenarios of area monitoring and compare it with a state-of-the-art algorithm.
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