复杂的分布式智能系统的光谱特性与环境相结合
Alexander P Alodjants1, Dmitriy V Tsarev1, Petr V Zakharenko1
1National Center for Cognitive Reaserch, ITMO University, St. Petersburg 197101, Russia.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
我们开发了一种量子启发的框架,用于模拟分布式智能系统 (DIS) 中的人工智能代理的集体行为. 我们的研究结果表明,网络结构和外部影响如何影响意见的一致性和决策.
科学领域:
- 复杂的系统复杂的系统.
- 人工智能的人工智能
- 量子计算是一种量子计算.
背景情况:
- 分布式智能系统 (DIS) 利用人工智能代理 (AIA) 和大型语言模型 (LLM) 进行集体任务.
- 在DIS中,复杂的网络拓引入了对建立共识和决策过程的不确定性.
- 了解外部影响如何与DIS结构相互作用,对于可预测的集体行为至关重要.
研究的目的:
- 提出一个量子启发的图形信号处理框架,用于在DIS中建模集体行为.
- 分析网络拓和外部影响对DIS动态的影响.
- 调查方法,以保持在LLM参与的DIS的连贯性.
主要方法:
- 使用无尺度和瓦茨-斯特罗格茨图表来建模DIS网络拓.
- 采用量子图形信号处理方法,使用影响矩阵 (IM) 代表外部环境.
- 分析了两个相互作用模式:对齐 (通勤) 和非对齐 (非通勤) 邻近矩阵和IM.
- 应用重新规范化组缩放和光谱用于分析.
主要成果:
- 在对齐的系统中,最小的外部影响会导致完全相位同步和连贯的动态.
- 带有负合的非通行影响引入光谱障碍,破坏相位连贯性.
- 主导的集体模式 (Perron模式) 尽管存在混乱,但仍然强大,尽管意见可能会分裂.
- 光谱有效量化了混乱和外部影响的程度.
结论:
- 拟议的框架为LLM参与的DIS的动态提供了洞察力.
- 网络拓和外部影响显著调节集体行为和意见的一致性.
- 可以制定策略来设计DIS,即使在环境干扰下也保持一致性.
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