通过知识图表管理工业5.0场景中的人类-人工智能合作:关键挑战和经验教训
Franz Krause1, Heiko Paulheim1, Elmar Kiesling2
1The Data and Web Science Group (DWS), University of Mannheim, Mannheim, Germany.
Frontiers in artificial intelligence
|November 26, 2024
概括
本研究介绍了知识图 (KG) 的后期塑造,以管理工业5.0制造业中的人类-人工智能协作. 这种方法允许在运行时动态适应和整合人类智能,以提高系统性能.
科学领域:
- 人工智能的人工智能
- 制造业 工程 制造工程
- 知识表示 知识表示
背景情况:
- 工业5.0强调了在制造业中的人类-人工智能协作.
- 传统的知识图 (KG) 系统通常使用静态设计,限制了运行时间的适应性.
- 在人工智能辅助制造中管理动态的人类干预需要灵活的方法.
研究的目的:
- 提出和评估工业5.0.0中的知识图 (KG) 的后期塑造设计原则.
- 解决工业环境中动态的人类-人工智能协作的挑战.
- 为了证明晚期塑造对于运行时适应和人类智能集成的有效性.
主要方法:
- 运用知识图 (KG) 来管理线内的人类干预.
- 实施KG结构和嵌入式的后期成型设计原则.
- 将关系机器学习模型应用于图形结构数据.
- 从欧洲Teaming.AI项目中吸取教训.
主要成果:
- 晚期塑造使粗细的方法成为可能,从而促进运行时间的适应.
- 工业KG的动态人口和嵌入的晚期塑造是至关重要的.
- 关系机器学习模型有效地利用图形数据进行洞察.
- 该Teaming.AI项目提供了关于垂直知识整合的实用见解.
结论:
- 晚期塑造对于工业5.0制造中的动态人-人工智能协作至关重要.
- 灵活的KG设计原则是整合在运行时的人类智能的关键.
- 这种方法通过允许适应不断变化的条件来提高整体系统性能.
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