Proceedings of the 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology

Modelling Human Vision for Heuristics

Authors
Sean McGerty, Frank Moisladis
Corresponding Author
Sean McGerty
Available Online December 2013.
DOI
https://doi.org/10.2991/visio-13.2014.7How to use a DOI?
Keywords
vision, attention, artificial intelligence, evolution, satisficing, particle swarm, genetic algorithm, simulated annealing, mutation.
Abstract
Human vision is a highly complex system that has evolved to enable interactions within and on our environment in an expedient and resource efficient way. Applying black box testing principles gives us insight to functional parameterizations within the brain. Neuroscience further helps us understand the localization of functions within the brain. By modeling these specializations we gain a taxonomy for interactions between focused and persistent attention modes. Using this taxonomy we break down the interactions within evolutionary heuristic showing possibilities for granular hybrid behaviors. Finally we correlate this approach in a byplay between strategic and tactical concerns in military simulation.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
The 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-94-6252-002-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/visio-13.2014.7How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Sean McGerty
AU  - Frank Moisladis
PY  - 2013/12
DA  - 2013/12
TI  - Modelling Human Vision for Heuristics
BT  - The 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/visio-13.2014.7
DO  - https://doi.org/10.2991/visio-13.2014.7
ID  - McGerty2013/12
ER  -