KI - Künstliche Intelligenz
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.
Health and Wellbeing
Health and Wellbeing: Our ageing population—what role will you play?
The EU28 population’s annual healthcare expenditure has risen to € 1,085 billion, a substantial share of which arises through secondary prevention, long-term care and home-care (€ 90 billion). These costs are increasing towards 2020, while the available budget and the number of caretakers are shrinking. In developed countries around the world, an ageing population is the new reality. It’s a reality that poses challenges to society, but also unique opportunities for artificial intelligence methods in health and wellbeing.
We should act on this challenge by offering AI-based solutions that respond to the demands for: (1) self-monitoring (quantified-self) related to health and habits, while these solutions will also reduce the costly demand for secondary prevention, cure and care for caretakers and insurers; (2) big health data analysis and clinical data intelligence for individualized treatment.
Themes of interest include, but are not limited to, the following areas of the patient/user centric view, the doctor/ clinical view, or the combination of these two views.
Patient/user centric view:
Smart unobtrusive sensing of vital body signals (of carehome residents)
Event/task extraction from (video) life logging
Data mining of contextual data
Personalised associations between health and behavior
Vital signs and context data fusion and correlation
Smart coaching algorithms (life-coaching) for wellbeing
Intelligent User Interfaces for health and wellbeing
Adaptive persuasion (based on a feedback loop) and persuasion technologies
Adaptable interfaces that understand the physical and cognitive abilities of the user
Adaptive interfaces that learn so that the user becomes more comfortable with the service over time
Personalised schemes for behavioural change
Health risk assessment for individuals and specific target groups
Agents and healthcare
Embodied conversational agents (ECAs) in healthcare.
Smart (unobtrusive) sensing of vital body signals in clinical environments
Data mining of contextual clinical data in different modalities (e.g., clinical records and medical images)
Semantic annotation of medical texts and images related to the ageing population
Text mining in the health and wellbeing domain
Big data analysis and clinical data intelligence
Personalised schemes for individualized treatment and medication
Formalising clinical guidelines for health and wellbeing
Contributions can be from the following categories (for more detailed information please refer to the author instructions for each of these categories): technical contributions, research projects, discussions, and book reviews.
If you intend to publish a paper, please contact the editors:
Jean H.A. Gelissen
Action Line Leader Health and Wellbeing EIT ICT Labs
High Tech Campus 69, 1C5656AG Eindhoven, The Netherlands
Dr. Daniel Sonntag
German Research Center for Artificial Intelligence
D-66123 Saarbruecken, Germany
Phone: +49 681857755254
We look forward to receiving your contribution!
Higher-Level Cognition and Computation
Human higher-level cognition is a multi-faceted and complex area of thinking which includes the mental processes of reasoning, decision making, creativity, and learning among others. Logic, understood as a normative theory of thinking, has a widespread and pervasive effect on the foundations of cognitive science. However, human reasoning cannot be completely described by logical systems. Sources of explanations are incomplete knowledge, incorrect beliefs, or inconsistencies. Still, humans have an impressive ability to derive satisficing, acceptable conclusions. Generally, people employ both inductive and deductive reasoning to arrive at beliefs; but the same argument that is inductively strong or powerful may be deductively invalid. Therefore, a wide range of reasoning mechanism has to be considered, such as analogical or defeasible reasoning.
The topics of interest include, but are not limited to:
• Analogical reasoning
Common sense and defeasible reasoning
Deductive calculi for higher-level cognition
• Inductive reasoning and cognition
• Preferred mental models and their formalization
• Probabilistic approaches of reasoning
The Künstliche Intelligenz journal, which is published and indexed by Springer, supports the following lists of formats: technical contributions, research projects, discussions, dissertation abstracts, conference reports and book reviews. If you are interested in contributing to this special issue, please contact one of the guest editors:
Dr. Marco Ragni
University of Freiburg
Center for Cognitive Science
Institute of Computer Science and Social Research
D-79098 Freiburg, Germany
Prof. Frieder Stolzenburg
Harz University of Applied Sciences
Automation & Computer Sciences Dep.
38855 Wernigerode, Germany
• Statement of interest: 15-Aug-2014
• Submission deadline: 07-Oct-2014
• Notification: 15-Nov-2014
• Camera-ready copy: 15-Jan-2015
• Special issue: KI 3/2015
Submission and contribution format:
The articles should be written in english, in order to attract an international audience, formatted with the Springer LaTeX package for journals (\http://www.static.springer.com/sgw/documents/468198/application/zip/LaTeX.zip[).
Submissions should be sent as pdf file to firstname.lastname@example.org.
Advances in Autonomous Learning
Autonomous Learning research aims at understanding how adaptive systems can efficiently learn from the interaction with the environment, especially by having an integrated approach to decision making and learning, allowing systems to decide by themselves on actions, representations, hyper-parameters and model structures for the purpose of efficient learning.
It addresses challenges such as how to autonomously learn representations for efficient model use, how to arrive at suitable cost functions from meta-objectives (generalizing inverse RL), how to autonomously choose model structures and hyper-parameters in possibly non-stationary environments, or how to design efficient actor-reward strategies which generalize across tasks.
Application scenarios which require these type of complex models span high-impact domains such as robotics, life-long learning, intelligent tutoring, or big data analytics. We invite contributions related to the following non exhaustive list of topics:
– autonomous learning of rich data representations,
– active learning in structured (e.g., hybrid, relational) interactive domains,
– learning models with autonomous complexity adaptation,
– transfer learning,
– structure learning,
– statistical relational learning,
– theoretical advances to measure model autonomy,
– applications and project reports in the field of autonomous learning.
Prof. Barbara Hammer
Prof. Marc Toussaint
At present, we observe a rapid growth in the development of increasingly complex ‘‘intelligent’’ systems that serve users throughout all areas of their daily life. They range from classical technical systems such as household devices, cars, or consumer electronics through mobile apps and services to advanced service robots in various fields of application. While many of the rather conventional systems already provide multiple modalities to interact with, the most advanced are even equipped with cognitive abilities such as perception, cognition, and reasoning. However, the use of such complex technical systems and in particular the actual exploitation of their rich functionality remain challenging and quite often lead to users’ cognitive overload and frustration.
Companion Technologies aim at bridging the gap between the extensive functionality of technical systems and human users’ individual requirements and needs. They enable the construction of really smart—adaptive, flexible, and cooperative—technical systems by employing a combination of AI techniques and relying on psychological and neurobiological findings.
The special issue ‘‘Companion Technologies’’ of the KI Journal aims to present ongoing research, application perspectives, and other insights into an exciting research area emerging across the fields of Artificial Intelligence, Cognitive Psychology, and Cognitive Sciences. Topics of interest include, but are not limited to:
• Computational models of cognitive processes
• Reasoning for adaptive systems
• User-centered planning
• Multi-modal emotion and motivation recognition
• Knowledge-based human–computer interaction
• Knowledge-based dialogue management
• Cooperative and adaptive systems
The KI Journal, published and indexed by Springer, supports a variety of formats including technical articles, project descriptions, surveys, dissertation abstracts, conference reports, and book reviews.
Interested authors are asked to contact the guest editors at their earliest convenience:
Prof. Dr. Susanne Biundo-Stephan
Institute of Artificial Intelligence
Institute of Artificial Intelligence
Institute of Artificial Intelligence
- 6 Volumes
- 21 Issues
- 361 Articles
- 11 Open Access
- 2010 - 2015 Available between
- Journal Title
- KI - Künstliche Intelligenz
- Volume 24 / 2010 - Volume 29 / 2015
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
- Industry Sectors
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