KI - Künstliche Intelligenz
German Journal on Artificial Intelligence - Organ des Fachbereichs "Künstliche Intelligenz" der Gesellschaft für Informatik e.V.
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.
Landmark-Based Navigation in Cognitive Systems
The importance of landmarks in human navigation has long been recognized in multiple fields. These include areas involved in the understanding, modelling and supporting wayfinding, spatial knowledge acquisition, and place recognition. From the Psychological, Linguistic and Cognitive Neuroscience viewpoint, the perceived landmarkness of discrete objects vary among individuals. Thus, the key challenge lies in identifying those properties, which remain relevant across a wide range of individual differences, experiences, and behavioural patterns. From the Computer Science, Artificial Intelligence and Cognitive Modelling perspective, formalising these relations in a manner successfully matching the landmark’s relevance for humans has proven difficult. Most recently, the increasing volume and accessibility of semantically rich geospatial data has opened new avenues for further progress in this area. The continuing collaboration between these fields is exemplified by the regular conference series on spatial information theory and geospatial science as well as multiple on-going interdisciplinary research projects.
In spite of that, technologies used to support human navigation struggle to incorporate the type of landmark information relevant for the human user. The gap between the human’s and the computer’s understanding of what constitutes a landmark remains one of the major challenges in the development of spatial systems intuitive in use as well as in modelling navigational behaviour similar to this of a human.
This special issue integrates theoretical, experimental and computational contributions from disciplines involved in the study of landmark-based navigation in cognitive systems. The aim of the issue is to identify new areas for potential interdisciplinary collaboration and we invite applications focusing on, but not limited to, the following topics:
– Automatic, semi-automatic, and crowd-sourced detection of landmarks.
– Modelling of landmark-based navigation.
– Landmark knowledge acquisition and use.
– Communication of landmark-ness.
– Landmark-based approaches for indoor navigation.
– Human-computer interaction with landmark-based systems.
– Ubiquitous computing applications of the landmark concept.
The KI Journal, which is published and indexed by Springer, supports the following lists of formats: Technical contributions, research projects, discussions, dissertation abstracts, conference reports, software, and book reviews. If you are interested in contributing to this special issue, please contact one of the guest editors:
Prof. Dr. Angela Schwering
Dr. Jakub Krukar
Vanessa Joy Anacta
Institute for Geoinformatics
University of Muenster
Smart Environments - Artificial Intelligence - Human-Machine-Interaction
Smart Environments aim to provide installations that support and enhance the abilities of humans in their regular life and possibly improve the environments themselves too, e.g., in terms of energy efficiency. Smart Environments are based on complex and distributed technical systems but they bear more challenges than the seamless composition of its components addressed in current research in technical disciplines. Setting technical challenges aside, providing intuitive interfaces to a system hidden in the environment and identifying means that allow the system automatically to provide suitable assistance for a wide range of every-day tasks involves several research questions. Smart Environments are best described as an active field of research spanning several disciplines, but particularly related to Artificial Intelligence and Human-Machine Interaction. Since all applications penetrate our daily life with sensors, privacy becomes a central issue, too. This special issue aims at presenting a survey of the manifold AI-related research currently being performed, along with presentations of the projects and labs in which new ideas are conceived. Among the various disciplines involved, this issue addresses AI-related aspects and the interplay of AI and human-machine interaction, in particular AI techniques fostering new kinds of interaction, shedding some light onto questions such as:
- How can a ubiquitous system communicate its state of believe to a human?
- How can Smart Environments quickly comprehend a situation and user needs?
- How can a system adapt to users and how adaptive should it be?
- Which forms of knowledge representation help to form a shared mental model of system and user
- How can user acceptance be measured efficiently and reliably?
- What are social and ethical implications of Smart Environments?
- Which are the most pressing use cases? What are their specific requirements?
We welcome submissions for all kinds of articles published by KI, but especially (a) genuine research articles (max. 7 pages in print incl. authors’ vita), (b) project presentations to highlight an important research question (4–6 pages), and (c) lab presentations to showcasing smart environment research installations and associated research (4–6 pages).
Please send your submissions via email directly to one of the guest editors listed below, please do not use the online submission system of the journal as otherwise submissions will not be associated with this special issue.
June 30, 2016: submission deadline
May 2017: journal publication
Please signal your interest in submitting a paper by sending a working title of your manuscript to the guest editors in advance.
Junior prof. Dr. Diedrich Wolter
University of Bamberg
Junior prof. Dr. Alexandra Kirsch
University of Tübingen
Media Informatics (Human-Computer Interaction)
Semantic Interpretation of Multi-Modal Human-Behaviour Data
This special issue of the KI journal focusses on and emphasizes general methods and tools for activity and event-based semantic interpretation of multi-modal sensory data relevant to a range of application domains and problem contexts where interpreting human behaviour is central. The overall motivation and driving theme of the special issue pertains to AI-based methods and tools that may serve a foundational purpose toward the high-level semantic interpretation of large-scale, dynamic, multimodal sensory data, or data streams. Data-sources that may be envisaged in
- Visio-spatial imagery
- Movement and interaction data
- Neurophysiological and other human behavior data
Proposed foundational methods will, for instance, present the development of human-centered technologies and cognitive interaction systems aimed at assistance and empowerment, e.g. in everyday life and professional problem solving and creativity. This call particularly emphasizes systematically formalized integrative AI methods and tools (e.g., combining reasoning and learning) that enable declarative modelling, reasoning and query answering, relational learning, embodied grounding and simulation etc. Broadly, the role of declarative abstraction, knowledge representation and reasoning, and neural-symbolic learning and inference from multi-modal sensory data is highly welcome.
For details, please refer to the full Call for Papers at http://hcc.uni-bremen.de/calls/SpecialIssue-KI, or contact one of the guest editors.
Prof. Dr. Mehul Bhatt
University of Bremen, Germany
Prof. Dr. Kristian Kersting
Technical University of Dortmund, Germany
Algorithmic Challenges and Opportunities of Big Data
Computer systems pervade all parts of human activity and acquire, process, and exchange data at a rapidly increasing pace. As a consequence, we live in a big data world where information is accumulating at an exponential rate and complexity, and often the real problem has shifted from collecting enough data to dealing with its impetuous growth and abundance when going through it to mine relevant or pertinent information. In fact, we often face poor scale-up behavior from algorithms that have been designed based on models of computation that are no longer realistic for big data. This implies challenges like algorithmic exploitation of parallelism (multicores, GPUs, parallel and distributed systems, etc.), handling external and outsourced memory as well as memory-hierarchies (clouds, distributed storage systems, hard-disks, flash-memory, etc.), dealing with large scale dynamic data updates and streams, compressing and processing compressed data, approximation and online processing respectively mining under resource constraints, increasing the robustness of computations (e.g., concerning data faults, inaccuracies, or attacks) or reducing the consumption of energy by algorithmic measures and learning. Only then big data will truly open up unprecedented opportunities for both scientific discoveries and commercial exploitation in Artificial Intelligence, Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, Agriculture, Life Sciences and Digital Libraries, among other domains.
The aim of the special issue is to collect overview articles on important state-of-the-art algorithmic foundations and applications, as well as articles on emerging trends for the future of big data.
Prof. Dr. Ulrich Meyer
Goethe Universität Frankfurt am Main, Germany
Institut für Informatik
- Journal Title
- KI - Künstliche Intelligenz
- Volume 24 / 2010 - Volume 31 / 2017
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
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