This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.
翻译:本文介绍了计算机物理生产系统人工智能的新型认知架构CAAI, 其宗旨是减少人工智能算法的使用。 CAAI的核心是一个认知模块,该模块处理用户的宣示目标,选择合适的模型和算法,并为在大数据平台上执行处理管道创建配置。根据性能标准不断观测和评价,评估许多不同用途案例的管道性能。根据这些评估,管道在必要时会自动调整。配有明确界定界面的模块设计使得管道组件的可重复性和可扩展性得以实现。一个大数据平台实施这一模块设计,由多克、库伯涅茨和卡夫卡等技术支持,用于个人组件及其通信的虚拟化和协同化。根据真实世界使用案例对结构的实施进行评估。