<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Butko, N.</style></author><author><style face="normal" font="default" size="100%">Movellan, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Infomax Control of Eye Movements</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Autonomous Mental Development</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">active information gathering</style></keyword><keyword><style  face="normal" font="default" size="100%">autonomous computer program</style></keyword><keyword><style  face="normal" font="default" size="100%">autonomous physical agent</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamic tracking task</style></keyword><keyword><style  face="normal" font="default" size="100%">Eye movement</style></keyword><keyword><style  face="normal" font="default" size="100%">eye movement strategy</style></keyword><keyword><style  face="normal" font="default" size="100%">face detection</style></keyword><keyword><style  face="normal" font="default" size="100%">faces</style></keyword><keyword><style  face="normal" font="default" size="100%">Infomax control</style></keyword><keyword><style  face="normal" font="default" size="100%">motor system</style></keyword><keyword><style  face="normal" font="default" size="100%">object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">optimal control</style></keyword><keyword><style  face="normal" font="default" size="100%">optimal eye movement controller</style></keyword><keyword><style  face="normal" font="default" size="100%">policy gradient</style></keyword><keyword><style  face="normal" font="default" size="100%">probabilistic model</style></keyword><keyword><style  face="normal" font="default" size="100%">sensory system</style></keyword><keyword><style  face="normal" font="default" size="100%">static scenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Perception</style></keyword><keyword><style  face="normal" font="default" size="100%">visual search</style></keyword><keyword><style  face="normal" font="default" size="100%">visual system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">91-107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;color: rgb(68, 68, 68); font-family: 'Lucida Grande', Verdana, sans-serif; font-size: 14px; background-color: rgba(0, 0, 0, 0.0470588);&quot;&gt;Recently, infomax methods of optimal control have begun to reshape how we think about active information gathering. We show how such methods can be used to formulate the problem of choosing where to look. We show how an optimal eye movement controller can be learned from subjective experiences of information gathering, and we explore in simulation properties of the optimal controller. This controller outperforms other eye movement strategies proposed in the literature. The learned eye movement strategies are tailored to the specific visual system of the learner-we show that agents with different kinds of eyes should follow different eye movement strategies. Then we use these insights to build an autonomous computer program that follows this approach and learns to search for faces in images faster than current state-of-the-art techniques. The context of these results is search in static scenes, but the approach extends easily, and gives further efficiency gains, to dynamic tracking tasks. A limitation of infomax methods is that they require probabilistic models of uncertainty of the sensory system, the motor system, and the external world. In the final section of this paper, we propose future avenues of research by which autonomous physical agents may use developmental experience to subjectively characterize the uncertainties they face.&lt;/span&gt;&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">91</style></section></record></records></xml>