Modeling Human Vision for Heuristics

被引：0

作者：

McGerty, Sean ^{[1
]}

Moisiadis, Frank ^{[1
]}

机构：

[1] Univ Notre Dame Australia, Fremantle, WA, Australia

来源：

PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS,VISUALIZATION, COMPUTER VISION, AND GAME TECHNOLOGY | 2013年 / 53卷

关键词：

vision; attention; artificial intelligence; evolution; satisficing; particle swarm; genetic algorithm; simulated annealing; mutation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：38 / 45

页数：8

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