用黑板架构让大模型多智能体动态协作，更高效解决复杂问题。
Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture
- 基于黑板架构实现智能体间实时信息共享与动态角色选择
- 在常识、推理和数学任务上平均表现最优，且消耗更少计算资源
- 适合无固定流程的复杂问题求解，尤其适用于动态协作场景
本文提出将黑板架构引入大语言模型多智能体系统，使不同角色的智能体在整个问题求解过程中可共享全部信息与消息；行动智能体根据黑板当前内容动态选择，并重复选择与执行轮次，直至黑板达成共识。我们实现了首个该架构的完整系统，在常识知识、推理和数学数据集上进行实验。结果表明，该系统在平均性能上可媲美当前最佳静态与动态多智能体系统，同时显著减少令牌消耗。该方案有望支持结构不明确或流程动态变化的复杂问题求解。
原文摘要 · Abstract (English)
In this paper, we propose to incorporate the blackboard architecture into LLM multi-agent systems (MASs) so that (1) agents with various roles can share all the information and others' messages during the whole problem-solving process, (2) agents that will take actions are selected based on the current content of the blackboard, and (3) the selection and execution round is repeated until a consensus is reached on the blackboard. We develop the first implementation of this proposal and conduct experiments on commonsense knowledge, reasoning and mathematical datasets. The results show that our system can be competitive with the SOTA static and dynamic MASs by achieving the best average performance, and at the same time manage to spend less tokens. Our proposal has the potential to enable complex and dynamic problem-solving where well-defined structures or workflows are unavailable.
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