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Computational Intelligence in Robotics is devoted to publishing leading-edge research that investigates the role of artificial and computational intelligence in the field of robotics.
The section aims to publish highly innovative, original, salient papers describing novel nature-inspired computational methodologies and drawing on rigorous quantitative approaches across computer science, mathematics, physics and life sciences for the application in robotic systems.
We encourage submissions on a wide range of research topics, relevant to the analysis and modelling of the intelligence phenomenon, including:
· guided self-organization,
· complex adaptive systems,
· embodied intelligence,
· swarm intelligence, artifical life and multi-agent systems,
· evolutionary computation and evolutionary game theory in robotics,
· information theory of cognitive systems,
· complex networks and distributed computation,
· computational robotics,
· bio-inspired cyber-physical security,
· morphological computation in modular robots,
· machine learning, pattern recognition and data mining for robotics.
This section aims to foster a community that encompasses the cross-disciplinary nature of modern computational intelligence developing tools with the intention of promoting and developing this exciting field of robotics.
Indexed in: Scopus, Google Scholar, DOAJ, CrossRef, dblp, Ulrich's Periodicals Directory, ESCI, Emerging Sources Citation Index, CLOCKSS
Computational Intelligence in Robotics welcomes submissions of the following article types: Brief Research Report, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review and Technology and Code.
All manuscripts must be submitted directly to the section Computational Intelligence in Robotics, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Computational Intelligence in Robotics will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Robotics and AI.
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For all queries regarding manuscripts in Review and potential conflicts of interest, please contact email@example.com
For queries regarding Research Topics, Editorial Board applications, and journal development, please contact firstname.lastname@example.org