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Learning distinctions and rules in a continuous world through active exploration (2007)
Jonathan Mugan
and
Benjamin Kuipers
We present a method that allows an agent through active exploration to autonomously build a useful representation of its environment. The agent builds the representation by iteratively learning distinctions and predictive rules using those distinctions. We build on earlier work in which we showed that by motor babbling an agent could learn a representation and predictive rules that by inspection appeared reasonable. In this paper we add active learning and show that the agent can build a representation that allows it to learn predictive rules to reliably control its hand and to achieve a simple goal.
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PDF
Citation:
In
Proceedings of the International Conference on Epigenetic Robotics (EpiRob-07)
2007.
Bibtex:
@inproceedings{Mugan-epirob-07, title={Learning distinctions and rules in a continuous world through active exploration}, author={Jonathan Mugan and Benjamin Kuipers}, booktitle={Proceedings of the International Conference on Epigenetic Robotics (EpiRob-07)}, url="http://www.cs.utexas.edu/users/ai-lab?Mugan-epirob-07", year={2007} }
People
Benjamin Kuipers
Formerly affiliated Faculty
kuipers [at] cs utexas edu
Jonathan Mugan
Ph.D. Alumni
jmugan [at] cs utexas edu
Areas of Interest
Bootstrap Learning
Robotics