Article | RobocCup-99 Team Descriptions. Simulation League | Between Teaching and Learning: Development of the Team Mainz Rolling Brains for the Simulation League of RoboCup 99

Title:
Between Teaching and Learning: Development of the Team Mainz Rolling Brains for the Simulation League of RoboCup 99
Author:
Daniel Polani: Johannes Gutenberg-Universität Mainz, Germany Thomas Uthmann: Johannes Gutenberg-Universität Mainz, Germany
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Year:
1999
Conference:
RobocCup-99 Team Descriptions. Simulation League
Issue:
004
Article no.:
018
Pages:
84-87
No. of pages:
4
Publication type:
Abstract and Fulltext
Published:
1999-12-15
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
Linköping Electronic Articles in Computer and Information Science
ISSN (online):
1401-9841
Publisher:
Linköping University Electronic Press; Linköpings universitet


The development of our team for RoboCup~99 is mainly oriented towards a transparent way of transferring explicit knowledge into the agent control and its combination with learning algorithms capable of fine-tuning the acquired skills. The explicit knowledge is formulated in terms of rules; the non-explicit knowledge is to be realized as a set of parameters adapted by hierarchical reinforcement learning and by rule evolution. The teaching process for the implicit learning is not determined by a simple fixed reinforcement return; but by a --- possibly complex --- agent that represents a human or an automated coach.

RobocCup-99 Team Descriptions. Simulation League

Author:
Daniel Polani, Thomas Uthmann
Title:
Between Teaching and Learning: Development of the Team Mainz Rolling Brains for the Simulation League of RoboCup 99
References:

[1] T. G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. Submitted to Machine Learning; 1999.


[2] G. Edelman. Neural Darwinism: Theory of Neuronal Group Selection. Basic Books; New York; 1987.


[3] Leslie Pack Kaelbling; Michael L. Littman; and Andrew W. Moore. Reinforcement learning: a survey. Journal of Arti cial Intelligence Research; 4:237{285; May 1996.


[4] Hitoshi Matsubara; Itsuki Noda; and Kazuo Hiraki. Learning of cooperative actions in multi-agent systems: a case study of pass in soccer. In AAAI-96 Spring Symposium on Adaptation; Coevolution and Learning in Multi-Agent Systems; pages 63{67; Mar 1996.


[5] D. Moriarty and R. Miikkulainen. Forming neural networks through ecient and adaptive co-evolution. Evolutionary Computation; 5:373{ 399; 1997.


[6] D. Polani and R. Miikkulainen. Fast reinforcement learning through eugenic neuro-evolution. Technical Report AI99-277; Department of Computer Sciences; The University of Texas at Austin; January 1999.


[7] R. S. Sutton and A. G. Barto. Reinforcement Learning. Bradford; 1998.

RobocCup-99 Team Descriptions. Simulation League

Author:
Daniel Polani, Thomas Uthmann
Title:
Between Teaching and Learning: Development of the Team Mainz Rolling Brains for the Simulation League of RoboCup 99
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