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English(EN) Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization

贝叶斯优化自动化多智能体系统设计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的贝叶斯进化优化框架，用于自动化多智能体系统（MAS）的设计和调整。该方法使用进化算子生成工作流图，并结合具有图感知高斯过程的贝叶斯优化来高效评估有前景的候选方案。该框架在代码生成的BigCodeBench上进行了测试，与其它智能体系统相比取得了优越的性能，并在使用TabPFN的表格数据集上进行了测试，显示出在预处理任务中的改进。 AI

影响 该框架通过自动化发现最优智能体拓扑结构，有望简化复杂AI系统的开发。

排序理由 该集群包含一篇学术论文，详细介绍了一种设计和调整多智能体系统的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

贝叶斯优化自动化多智能体系统设计

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该集群包含一篇学术论文，详细介绍了一种设计和调整多智能体系统的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dominik Jehle, Tom Zehle ·

    Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization

    arXiv:2610.29276v1 Announce Type: new Abstract: Designing effective multi-agent systems (MAS) remains a challenging and resource-intensive task. We propose a Bayesian Evolutionary Optimization framework for automatically discovering task-specific MAS topologies. The approach comb…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Tom Zehle ·

    Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization

    Designing effective multi-agent systems (MAS) remains a challenging and resource-intensive task. We propose a Bayesian Evolutionary Optimization framework for automatically discovering task-specific MAS topologies. The approach combines evolutionary operators to generate diverse …

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