分布式智能系统的量子启发型建模与人工智能代理人自组织自组织
A P Alodjants1, D V Tsarev1, A E Avdyushina1
1ITMO University, St. Petersburg, Russia, 197101.
Scientific reports
|July 4, 2024
概括
本研究介绍了分布式智能系统的量子启发模型,通过将人工智能代理调整为自然用户来提高用户满意度. 它揭示了AIA和NIA如何相互作用,通过阶段过渡影响论形成和社会影响.
科学领域:
- 综合自然和人工智能的跨学科研究.
- 复杂系统和网络理论.
- 量子启发的计算模型.
背景情况:
- 分布式情报系统 (DIS) 利用自然智能代理 (NIA) 和人工智能代理 (AIA) 进行决策.
- 当前的DIS面临着优化用户满意度和管理代理商之间的信息流的挑战.
- 了解人类-人工智能网络中的集体行为和意见形成对于数字化至关重要.
研究的目的:
- 提出一种新的量子启发方法来分析由NIA和AIA组成的DIS.
- 调查NIA-AIA互动中的合作性参数的作用及其对决策的影响.
- 探索DIS内部的意见形成和社会影响动态,专注于集体情绪状态.
主要方法:
- 开发了一种量子启发模型,将NIA认知状态 (价值激发) 映射到一个双层量子系统中.
- 分析了avatar-avatar网络,其中avatar传输信息,用户提供反 (喜欢/不喜欢).
- 引入了一般化的合作性参数,以量化NIA-AIA通信和信息丢失.
主要成果:
- 确定了第二阶段的非平衡阶段过渡,控制了意见形成和社会影响.
- 证明在值 (高合作性) 以上,稳定的信息领域出现,导致适应.
- 显示在值 (弱合) 下,信息传播被抑制,抑制了意见形成,但使AIA能够自我组织.
结论:
- 这项研究提供了一个框架,用于理解DIS中的集体行为,使用量子启发的原理.
- AIA和NIA之间的自适应性合率可以增强决策过程和用户影响.
- 这些发现为AIA作为人类-AI协作环境中的有效队友提供了新的视角.
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