The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

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Ontologies and Data Management Part 2, Vol. 34, Issue 4

This special issue focuses on the theory and practice of applying ontologies in data management (ODM), which is a research topic of significant interest in Knowledge Representation and Reasoning (KR&R) and Database Theory. Modern data-centric software systems need to handle data that is often heterogeneous, sensitive, very large, and even incomplete or inconsistent. Moreover, it often has very complex structures. Thus, the development of proper tools and techniques to handle this complexity is a pressing task. Ontologies in combination with automated reasoning are acknowledged as a promising tool to address some of these challenges, and thus are receiving significant attention both among researchers and industry. For instance, the prominent data integration paradigm called ontology-based data access (OBDA) suggests the use of ontologies to provide a conceptual view of a problem domain, where various possibly heterogeneous data sources can be linked to the same ontology using mappings, enabling users to pose queries using the ontology vocabulary. For query answering in OBDA, automated reasoning is used to compile information from the sources, possibly employing the domain knowledge in the ontology to infer new information.

Developmental Robotics, Vol. 35, Issue 1

Human intelligence develops through experience, robot intelligence is engineered -- is it? At least in the mainstream approaches based on classical Artificial Intelligence (AI) and Machine Learning (ML) the robotic engineering approach is pursued and data- or knowledge-based algorithms are designed to improve a robot's problem-solving performance. Based on this engineering perspective of classical AI/ML approaches plenty of valuable applicationspecific impact has been achieved. Yet, the achievements are often subject to restrictions that involve domain knowledge as well as constraints concerning application domains and computational hardware. Developmental Robotics seeks to extend this constrained perspective of engineered artificial robotic cognition, by building on inspiration from biological developmental processes to design robots that learn in an open-ended continuous fashion. Developmental Robotics considers cognitive domains that involve problem-solving, self-perception, developmental disorders and embodied cognition. This perspective helps to improve the performance of intelligent robotic agents, and it has already led to significant contributions that inspired cutting-edge application-oriented Machine Learning technology. In addition, Developmental Robotics also provides functional computational models that help to understand and to investigate embodied cognitive processes.

Education in Artificial Intelligence K-12, Vol. 35, Issue 2

The upcoming special issue of the KI Magazin addresses the emerging topic of education in Artificial Intelligence (AI) at the K-12 level. In recent years, Artificial Intelligence (AI) has attracted a lot of attention from the public, and become a major topic of economic and soc ietal discussion. AI already has a significant influence on various areas of life and across different sectors and fields. The speed and force with which AI is impacting our work and everyday life poses a tremendous challenge for our society and educational system. Teaching fundamental AI concepts and techniques has traditionally been done at the university level. However, in recent years several initiatives and projects pursuing the mission of K-12 AI education have emerged. In this context we also see education organizations and AI experts as well as governments developing and deploying AI-curricula and programs for a K-12 audience. The aim of this special issue is to provide a compact overview over this growing field. We invite contributions from researchers, practitioners, and educators interested in education in AI at K-12 level. If you are interested in submitting a paper, please contact one of the guest editors.