XAgents用规则和动态图提升多智能体协作效率
XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph
- 基于极性任务图与条件规则实现动态任务规划
- 在三组数据集上超越现有单/多智能体方法
- 适合复杂任务中需可靠协作的场景
大语言模型的快速发展显著提升了多智能体系统在支持人类完成复杂现实任务方面的能力。然而，面对高度复杂且存在不确定性的任务时，多智能体系统仍面临有效任务规划的挑战，常导致误导性或错误输出，阻碍任务执行。为此，我们提出XAgents，一个基于多极性任务处理图和IF-THEN规则的统一多智能体协作框架。XAgents利用多极性任务处理图实现动态任务规划并应对任务不确定性；在子任务处理过程中，引入领域特定的IF-THEN规则以约束智能体行为，同时通过全局规则增强智能体间协作。我们在三个不同数据集上评估了XAgents的性能，结果表明其在知识型与逻辑型问答任务中均持续优于当前最先进单智能体及多智能体方法。XAgents代码已开源：https://github.com/AGI-FHBC/XAgents。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of Multi-Agent Systems (MAS) in supporting humans with complex, real-world tasks. However, MAS still face challenges in effective task planning when handling highly complex tasks with uncertainty, often resulting in misleading or incorrect outputs that hinder task execution. To address this, we propose XAgents, a unified multi-agent cooperative framework built on a multipolar task processing graph and IF-THEN rules. XAgents uses the multipolar task processing graph to enable dynamic task planning and handle task uncertainty. During subtask processing, it integrates domain-specific IF-THEN rules to constrain agent behaviors, while global rules enhance inter-agent collaboration. We evaluate the performance of XAgents across three distinct datasets, demonstrating that it consistently surpasses state-of-the-art single-agent and multi-agent approaches in both knowledge-typed and logic-typed question-answering tasks. The codes for XAgents are available at: https://github.com/AGI-FHBC/XAgents.
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