Measuring computational awareness in contextual neural networks

被引：0
|
作者：
Huk, Maciej [1 ]
机构：
[1] Wroclaw Univ Technol, Dept Informat Syst, Wroclaw, Poland
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2016年
关键词：
computational awareness; contextual neural networks; selective attention; generalized backpropagation;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Modeling awareness is an important topic in the computer science as it is closely related to preparing systems that know what is needed (e.g. data accumulated or ignored, effector activated) to achieve a given goal. Preparing tools to build and compare dedicated or general aware computational systems can lead to step-by-step hierarchical construction of intelligent solutions. Within this text we show the relation between awareness, selective attention and contextual systems. Using this as a base we propose basic measures of awareness and present example numerical results obtained for selected contextual neural networks and dedicated, multi-problem benchmark sets. The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case.
引用
收藏
页码：2254 / 2259
页数：6
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