KI - Künstliche Intelligenz, volume 29, issue 4, pages 389-399

Geometric Design Principles for Brains of Embodied Agents

Nihat Ay 1, 2, 3
1
 
Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany | 
2
 
3
 
Santa Fe institute, Santa Fe, USA | 
Publication type: Journal Article
Publication date: 2015-07-22
Q3
Q2
SJR: 0.649
CiteScore: 8.6
Impact factor: 2.8
ISSN: 09331875, 16101987
Artificial Intelligence
Abstract
I propose a formal model of the sensorimotor loop and discuss corresponding extrinsic embodiment constraints and the intrinsic degrees of freedom. These degrees constitute the basis for adaptation in terms of learning and should therefore be coupled with the embodiment constraints. Notions of sufficiency and embodied universal approximation allow us to formulate principles for such a coupling. This provides a geometric approach to the design of control architectures for embodied agents.
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GOST Copy
Ay N. Geometric Design Principles for Brains of Embodied Agents // KI - Künstliche Intelligenz. 2015. Vol. 29. No. 4. pp. 389-399.
GOST all authors (up to 50) Copy
Ay N. Geometric Design Principles for Brains of Embodied Agents // KI - Künstliche Intelligenz. 2015. Vol. 29. No. 4. pp. 389-399.
RIS |
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RIS Copy
TY - JOUR
DO - 10.1007/s13218-015-0382-z
UR - https://doi.org/10.1007/s13218-015-0382-z
TI - Geometric Design Principles for Brains of Embodied Agents
T2 - KI - Künstliche Intelligenz
AU - Ay, Nihat
PY - 2015
DA - 2015/07/22
PB - Springer Nature
SP - 389-399
IS - 4
VL - 29
SN - 0933-1875
SN - 1610-1987
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2015_Ay,
author = {Nihat Ay},
title = {Geometric Design Principles for Brains of Embodied Agents},
journal = {KI - Künstliche Intelligenz},
year = {2015},
volume = {29},
publisher = {Springer Nature},
month = {jul},
url = {https://doi.org/10.1007/s13218-015-0382-z},
number = {4},
pages = {389--399},
doi = {10.1007/s13218-015-0382-z}
}
MLA
Cite this
MLA Copy
Ay, Nihat, et al. “Geometric Design Principles for Brains of Embodied Agents.” KI - Künstliche Intelligenz, vol. 29, no. 4, Jul. 2015, pp. 389-399. https://doi.org/10.1007/s13218-015-0382-z.
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