In this paper, we aim to evaluate multi-agent systems against complex dependencies, including spatial, causal, and temporal constraints. First, we construct a new benchmark, named VillagerBench, within the Minecraft environment.VillagerBench comprises diverse tasks crafted to test various aspects of multi-agent collaboration, from workload distribution to dynamic adaptation and synchronized task execution. Second, we introduce a Directed Acyclic Graph Multi-Agent Framework VillagerAgent to resolve complex inter-agent dependencies and enhance collaborative efficiency. This solution incorporates a task decomposer that creates a directed acyclic graph (DAG) for structured task management, an agent controller for task distribution, and a state manager for tracking environmental and agent data. Our empirical evaluation on VillagerBench demonstrates that VillagerAgent outperforms the existing AgentVerse model, reducing hallucinations and improving task decomposition efficacy. The results underscore VillagerAgent's potential in advancing multi-agent collaboration, offering a scalable and generalizable solution in dynamic environments. The source code is open-source on GitHub (https://github.com/cnsdqd-dyb/VillagerAgent).

翻译：本文旨在评估多智能体系统在复杂依赖关系（包括空间、因果和时间约束）下的性能。首先，我们在《我的世界》环境中构建了一个名为VillagerBench的新基准测试。VillagerBench包含多种任务，旨在测试多智能体协作的各个方面，从工作负载分配到动态适应和同步任务执行。其次，我们提出了一种基于有向无环图的多智能体框架VillagerAgent，以解决智能体间复杂的依赖关系并提升协作效率。该框架包含一个用于创建结构化任务管理有向无环图（DAG）的任务分解器、一个用于任务分配的智能体控制器，以及一个用于跟踪环境和智能体数据的状态管理器。我们在VillagerBench上的实证评估表明，VillagerAgent优于现有的AgentVerse模型，减少了幻觉并提高了任务分解效能。结果凸显了VillagerAgent在推进多智能体协作方面的潜力，为动态环境提供了一个可扩展且可泛化的解决方案。源代码已在GitHub上开源（https://github.com/cnsdqd-dyb/VillagerAgent）。