Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.

翻译：多智能体系统在复杂协作任务中优于单智能体。然而，在大规模场景中，确保分散式任务执行期间的信息及时交换仍是一项挑战。本文提出了一种在线分散式协调方案，用于处理复杂局部任务和间歇通信约束下的多智能体系统。与现有强制要求全时或间歇性连接的策略不同，我们的方法允许智能体根据其在线任务执行的最优性，在非周期性时间间隔内加入或离开通信网络。该方案同时确定局部计划并优化通信策略，即团队在何时何地通信。我们在智能体间建立了一个分散式势博弈模型，并通过在线局部搜索迭代生成纳什均衡。该均衡保证了局部任务的完成和间歇性通信约束的满足。我们针对多个强基准方法进行了广泛的数值仿真实验。