With the proliferation of AI-enabled software systems in smart manufacturing,
the role of such systems moves away from a reactive to a proactive role that
provides context-specific support to manufacturing operators. In the frame of
the EU funded this http URL project, we identified the monitoring of teaming
aspects in human-AI collaboration, the runtime monitoring and validation of
ethical policies, and the support for experimentation with data and machine
learning algorithms as the most relevant challenges for human-AI teaming in
smart manufacturing. Based on these challenges, we developed a reference
software architecture based on knowledge graphs, tracking and scene analysis,
and components for relational machine learning with a particular focus on its
scalability. Our approach uses knowledge graphs to capture product- and process
specific knowledge in the manufacturing process and to utilize it for
relational machine learning. This allows for context-specific recommendations
for actions in the manufacturing process for the optimization of product
quality and the prevention of physical harm. The empirical validation of this
software architecture will be conducted in cooperation with three large-scale
companies in the automotive, energy systems, and precision machining domain. In
this paper we discuss the identified challenges for such a reference software
architecture, present its preliminary status, and sketch our further research
vision in this project.

这篇论文研究在智能制造中的人工智能协作所涉及的挑战，提出了一个以知识图谱、跟踪和场景分析为基础的软件架构，并使用关系化机器学习对生产过程中的知识进行捕捉，从而进行上下文特定的行动推荐，以实现优化产品质量和防止身体伤害。

智能制造中人工智能协作的参考软件架构

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

We present discriminative Gaifman models, a novel family of relational
machine learning models. Gaifman models learn feature representations bottom up
from representations of locally connected and bounded-size regions of knowledge
bases (KBs). Considering local and bounded-size neighborhoods of knowledge
bases renders logical inference and learning tractable, mitigates the problem
of overfitting, and facilitates weight sharing. Gaifman models sample
neighborhoods of knowledge bases so as to make the learned relational models
more robust to missing objects and relations which is a common situation in
open-world KBs. We present the core ideas of Gaifman models and apply them to
large-scale relational learning problems. We also discuss the ways in which
Gaifman models relate to some existing relational machine learning approaches.

通过对知识库局部的关联和有界大小的区域的表示进行底层特征表示学习，且对知识库的局部和有界大小的邻域进行考虑，我们提出了一种判别式的 Gaifman 模型，从而使学习到的关系模型更具鲁棒性。

判别式盖夫曼模型

Discriminative Gaifman Models

Relational machine learning studies methods for the statistical analysis of
relational, or graph-structured, data. In this paper, we provide a review of
how such statistical models can be "trained" on large knowledge graphs, and
then used to predict new facts about the world (which is equivalent to
predicting new edges in the graph). In particular, we discuss two fundamentally
different kinds of statistical relational models, both of which can scale to
massive datasets. The first is based on latent feature models such as tensor
factorization and multiway neural networks. The second is based on mining
observable patterns in the graph. We also show how to combine these latent and
observable models to get improved modeling power at decreased computational
cost. Finally, we discuss how such statistical models of graphs can be combined
with text-based information extraction methods for automatically constructing
knowledge graphs from the Web. To this end, we also discuss Google's Knowledge
Vault project as an example of such combination.

本文介绍了关系机器学习中如何统计建模大型知识图谱，以预测世界中新的事实，包括潜在特征模型和图中可观察模式互补的统计关系建模方法，以及与文本信息抽取相结合，自动构建知识图谱的实践。