German Journal of Artificial Intelligence: Special Issue on Developmental robotics
KI-SI-DR 2020
- URL: https://www.springer.com/journal/13218/updates/17545904
- Call For Paper Type: SI
- H2 Index: 0
- Submission Date: 2020-03-15
Special Issue Guest Editors:
Manfred Eppe, Verena V. Hafner, Yukie Nagai, Stefan Wermter
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 application-specific 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.
For this special issue, we welcome contributions that include, but are not limited to the following topics:
Robotic self-perception and body representation; Typical development and developmental disorders; Neural foundations of development and learning; Continual learning; Transfer learning; Embodied cognition; Problem-solving; Predictive models; Intrinsic motivation; Language learning.
Contributions can be from the following categories (for more detailed information please refer to the author instructions for each of these categories):
Technical Contribution; System Descriptions; Project Reports; Dissertation and Habilitation Abstracts; AI Transfer; Discussion
If you are interested in contributing to this special issue, please contact the guest editors:
eppe@informatik.uni-hamburg.de
hafner@informatik.hu-berlin.de
nagai.yukie@mail.u-tokyo.ac.jp
wermter@informatik.uni-hamburg.de
Handling Editor, Editorial Board
Britta Wrede (Bielefeld University, Germany)
bwrede@techfak.uni-bielefeld.de
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