<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Speeding up Learning with Dynamic Environment
Shaping in Evolutionary Robotics</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Nicolas</mods:namePart><mods:namePart type="family">Bredeche</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Louis</mods:namePart><mods:namePart type="family">Hugues</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Evolutionary Robotics is a promising approach
to automatically build efficient controllers
using stochastic optimization techniques.
However, works in this area are often
confronted to complex environments where
even simple tasks cannot be achieved. In
the scope of this paper, we propose an approach
based on explicit problem decomposition
and dynamic environment shaping to
ease the learning task.</mods:abstract><mods:classification authority="lcc">Machine Learning</mods:classification><mods:classification authority="lcc">Robotics</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2005</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Lund University Cognitive Studies</mods:publisher></mods:originInfo><mods:genre>Conference Poster</mods:genre></mods:mods>