The development of high-performance machine learning models has traditionally required extensive expertise, thereby excluding domain experts without a formal AI background. To overcome this barrier, we propose LangBO, a novel framework that systematically integrates domain-specific prior knowledge into the Bayesian Optimization (BO) process via natural language input. By leveraging Large Language Models (LLMs) in combination with Retrieval-Augmented Generation (RAG) and a self-evaluation mechanism, unstructured domain expert knowledge is transformed into a structured Dirichlet prior distribution, thereby guiding the optimization of neural architectures and hyperparameters. Initial experiments on a real-world classification task demonstrate accelerated convergence without compromising final model performance while improving interpretability and sample-efficient AutoML for users lacking machine learning expertise.

The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Applied to a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.

Heterogeneous teams of autonomous robots offer a number of benefits for a variety of applications. But deploying such robots is a complex task that requires machine-interpretable descriptions in order to be flexible and adaptable. Formal descriptions in the form of ontologies are increasingly used to describe the functions of such autonomous robots in the form of capabilities and skills. However, these ontological descriptions and a corresponding invocation interface for skills need to be created, causing additional efforts for developers which are complex, time-consuming and error-prone. This contribution presents a Python framework that automates all these additional efforts. It supports a developer in implementing functionalities as skills by having them program only the skill behavior. The framework automatically takes care of generating a standardized state machine, an invocation interface and an ontological description. The presented framework can be used to implement arbitrary functionalities as skills using Python. This is demonstrated using two different evaluation case studies: a simplified behavior of a mobile robot as well as a machine learning algorithm used as an analytical skill for quality control. Both are integrated into an existing skill execution system and can interact with other skills based on their ontological description.

Artificial Intelligence (AI) applications in automation systems are usually distributed systems whose development and integration involve several experts. Each expert uses its own domain-specific modeling language and tools to model the system elements. An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist. As a result, there is often a lack of interdisciplinary system understanding, leading to increased development, integration, and maintenance efforts. This paper therefore presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level. This makes it possible to subdivide individual subareas into domain specific subsystems and thus reduce the existing efforts.