通过知识图谱管理工业 5.0 场景中的人机协作:主要挑战和经验教训。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1247712
Franz Krause, Heiko Paulheim, Elmar Kiesling, Kabul Kurniawan, Maria Chiara Leva, Hector Diego Estrada-Lugo, Gernot Stübl, Nazim Kemal Üre, Javier Dominguez-Ledo, Maqbool Khan, Pedro Demolder, Hans Gaux, Bernhard Heinzl, Thomas Hoch, Jorge Martinez-Gil, Agastya Silvina, Bernhard A Moser
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引用次数: 0

摘要

在本文中,我们讨论了基于知识图谱(KG)的技术和方法,这些技术和方法能够在潜在变化的条件下管理工业 5.0 人工智能辅助制造流程中的在线人工干预,以保持或提高整体系统性能。基于知识图谱的系统通常以静态视角为基础,其结构在设计时就已固定,而我们认为,工业环境中人与人工智能(H-AI)在线协作所面临的动态挑战要求采用后期塑造的设计原则。早期塑造是在设计时以细粒度的方式确定系统的行为,与之相比,后期塑造是一种从粗到细的方法,为运行时的微调、适应和整合人类智能留下了更多空间。在此背景下,我们讨论了欧洲制造项目 Teaming.AI 的方法和经验教训,https://www.teamingai-project.eu/,以解决领域专业技术建模等一般挑战,特别关注垂直知识集成,以及与工业 KG 选择相关的挑战,如其动态群体和 KG 嵌入的后期塑造,作为关系型机器学习模型的基础,该模型已成为利用图结构数据推断新见解的有效工具。
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Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned.

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

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