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IROS
2008
IEEE
203views Robotics» more  IROS 2008»
16 years 1 months ago
Learning equivalent action choices from demonstration
Abstract— In their interactions with the world robots inevitably face equivalent action choices, situations in which multiple actions are equivalently applicable. In this paper, ...
Sonia Chernova, Manuela M. Veloso
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
16 years 5 days ago
A statistical learning theory approach of bloat
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of sy...
Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche, ...
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
16 years 5 days ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
EUROMICRO
2000
IEEE
15 years 11 months ago
Supporting Cooperative Learning of Process Knowledge on the World Wide Web
The WWW makes learning materials widely accessible and provides an environment where people can learn across time and space. However, the simple read-only information structure on...
Weigang Wang, Jörg M. Haake, Jessica Rubart, ...
GECCO
2000
Springer
143views Optimization» more  GECCO 2000»
15 years 10 months ago
A Genetic Algorithm for Automatically Designing Modular Reinforcement Learning Agents
Reinforcement learning (RL) is one of the machine learning techniques and has been received much attention as a new self-adaptive controller for various systems. The RL agent auto...
Isao Ono, Tetsuo Nijo, Norihiko Ono