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» Adapting RBF Neural Networks to Multi-Instance Learning
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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 7 days ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
AR
2008
188views more  AR 2008»
13 years 7 months ago
Intentional Control for Planetary Rover SRR
Intentional behavior is a basic property of intelligence and it incorporates the cyclic operation of prediction, testing by action, sensing, perceiving, and assimilating the exper...
Robert Kozma, Terry Huntsberger, Hrand Aghazarian,...
AROBOTS
1999
110views more  AROBOTS 1999»
13 years 7 months ago
Self-Localization of Autonomous Robots by Hidden Representations
We present a framework for constructing representations of space in an autonomous agent which does not obtain any direct information about its location. Instead the algorithm relie...
J. Michael Herrmann, Klaus Pawelzik, Theo Geisel
TNN
2010
176views Management» more  TNN 2010»
13 years 2 months ago
On the weight convergence of Elman networks
Abstract--An Elman network (EN) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer (feedback from the hidden layer). Therefo...
Qing Song
GECCO
2007
Springer
172views Optimization» more  GECCO 2007»
14 years 1 months ago
Acquiring evolvability through adaptive representations
Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be ...
Joseph Reisinger, Risto Miikkulainen