This article presents a theoretical framework for adapting the Common Model
of Cognition to large generative network models within the field of artificial
intelligence. This can be accomplished by restructuring modules within the
Common Model into shadow production systems that are peripheral to a central
production system, which handles higher-level reasoning based on the shadow
productions' output. Implementing this novel structure within the Common Model
allows for a seamless connection between cognitive architectures and generative
neural networks.

该文章提出了将认知通用模型调整为人工智能领域中的大型生成网络模型的理论框架。通过将通用模型中的模块重组为辅助于中心生产系统的阴影生成系统，可以实现这一目标，该中心生产系统基于阴影生成系统的输出进行高级推理。在通用模型中实现这种创新结构，可以实现认知体系结构和生成神经网络之间无缝的连接。

将生成网络与认知的共同模型相结合

Bridging Generative Networks with the Common Model of Cognition

A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.

描述了一种基于分布式网络模型的重叠社区检测方法，使用了快速的期望最大化算法对数百万节点的网络进行分析，可用于提取非重叠的社区划分。