为可靠的自动化提供混合智能系统:通过可扩展的AI架构推进知识工作和自主运营
Allan Grosvenor1, Anton Zemlyansky1, Abdul Wahab1
1MSBAI, Los Angeles, CA, United States.
Frontiers in robotics and AI
|August 1, 2025
概括
一个新的混合智能系统结合了符号推理和机器学习来实现关键任务的自动化. 这种方法提高了复杂任务的可解释性,适应性和可扩展性,如空间领域的认识和工程模拟.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 认知科学 认知科学
背景情况:
- 关键任务自动化需要可解释,适应和可扩展的决策,这是传统的象征性或数据驱动方法难以提供的.
- 现有的方法缺乏复杂,端到端自主工作流程所需的综合能力.
研究的目的:
- 引入一种新的混合智能 (H-I) 系统,该系统将象征性推理与先进的机器学习融合在一起.
- 开发一种由认知框架启发的等级架构,用于增强自主操作.
主要方法:
- 一个三级层次的层次架构,集成视觉转换器,基于图的神经网络,自动机器学习 (AutoML),联合嵌入预测架构 (JEPA) 和强化学习.
- 通过文件系统,数据库和接口进行导航;通过训练有素的代理进行离散的决策;并通过规划算法进行自适应性工作流程编排.
主要成果:
- 在空间域意识中展示了近实时异常检测,使用基于图形的JEPA和多代理强化学习的0.98精度回忆得分.
- 在计算流体动力学 (CFD) 的自主驱动模拟设置中取得了显著的改进,提高了边界层捕获传播和降低了几何失败率.
- 在2,000个计算节点中验证了线性可扩展性,用于人工智能模型训练,在跨域设置中具有最小的延迟.
结论:
- 混合智能系统提供了一个强大的和可转移的框架,用于在关键任务领域自动化复杂的知识工作.
- 这种方法为下一代工业人工智能系统在太空运营,工程模拟和自主设施管理方面铺平了道路.
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