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References
- 1
-
Sachiyo Arai, Kazuteru Miyazaki, and Shigenobu Kobayashi.
Generating cooperative behavior by multi-agent reinforcement
learning.
In Sixth European Workshop on Learning Robots, Brighton, UK,
August 1997.
- 2
-
Minoru Asada, Shoichi Noda, Sukoya Tawaratumida, and Koh Hosoda.
Purposive behavior acquisition for a real robot by vision-based
reinforcement learning.
Machine Learning, 23:279-303, 1996.
- 3
-
J. A. Boyan and M. L. Littman.
Packet routing in dynamically changing networks: A reinforcement
learning approach.
In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances
In Neural Information Processing Systems 6. Morgan Kaufmann Publishers,
1994.
- 4
-
Leslie Pack Kaelbling, Anthony R. Cassandra, and Michael L. Littman.
Acting optimally in partially observable stochastic domains.
In Proceedings of the Twelfth National Conference on Artificial
Intelligence, 1994.
- 5
-
Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore.
Reinforcement learning: A survey.
Journal of Artificial Intelligence Research, 4:237-285, May
1996.
- 6
-
Hiroaki Kitano, Yasuo Kuniyoshi, Itsuki Noda, Minoru Asada, Hitoshi Matsubara,
and Eiichi Osawa.
RoboCup: A challenge problem for AI.
AI Magazine, 18(1):73-85, Spring 1997.
- 7
-
Michael L. Littman.
Markov games as a framework for multi-agent reinforcement learning.
In Proceedings of the Eleventh International Conference on
Machine Learning, pages 157-163, San Mateo, CA, 1994. Morgan Kaufman.
- 8
-
Sean Luke, Charles Hohn, Jonathan Farris, Gary Jackson, and James Hendler.
Co-evolving soccer softbot team coordination with genetic
programming.
In Hiroaki Kitano, editor, RoboCup-97: The First Robot World
Cup Soccer Games and Conferences, Berlin, 1998. Springer Verlag.
In Press.
- 9
-
Maja J. Mataric.
Interaction and intelligent behavior.
MIT EECS PhD Thesis AITR-1495, MIT AI Lab, August 1994.
- 10
-
Itsuki Noda, Hitoshi Matsubara, and Kazuo Hiraki.
Learning cooperative behavior in multi-agent environment: a case
study of choice of play-plans in soccer.
In PRICAI'96: Topics in Artificial Intelligence (Proc. of 4th
Pacific Rim International Conference on Artificial Intelligence, Cairns,
Australia), pages 570-579, Cairns,Australia, August 1996.
- 11
-
J. Ross Quinlan.
C4.5: Programs for Machine Learning.
Morgan Kaufmann, San Mateo, CA, 1993.
- 12
-
Rafal P. Salustowicz, Marco A. Wiering, and Jurgen Schmidhuber.
Learning team strategies: Soccer case studies.
Machine Learning, 1998.
To appear.
- 13
-
Peter Stone and Manuela Veloso.
Multiagent systems: A survey from a machine learning perspective.
Technical Report CMU-CS-97-193, Computer Science Department, Carnegie
Mellon University, Pittsburgh, PA, December 1997.
- 14
-
Peter Stone and Manuela Veloso.
A layered approach to learning client behaviors in the RoboCup
soccer server.
Applied Artificial Intelligence, 12, 1998.
In Press.
- 15
-
Peter Stone and Manuela Veloso.
Towards collaborative and adversarial learning: A case study in
robotic soccer.
International Journal of Human-Computer Systems, 48, 1998.
In Press.
- 16
-
Peter Stone and Manuela Veloso.
Using decision tree confidence factors for multiagent control.
In Hiroaki Kitano, editor, RoboCup-97: The First Robot World
Cup Soccer Games and Conferences. Springer Verlag, Berlin, 1998.
In Press.
- 17
-
Ming Tan.
Multi-agent reinforcement learning: Independent vs. cooperative
agents.
In Proceedings of the Tenth International Conference on Machine
Learning, pages 330-337, 1993.
- 18
-
Manuela Veloso, Peter Stone, Kwun Han, and Sorin Achim.
The CMUnited-97 small-robot team.
In Hiroaki Kitano, editor, RoboCup-97: The First Robot World
Cup Soccer Games and Conferences. Springer Verlag, Berlin, 1998.
- 19
-
Jieyu Zhao and Jurgen Schmidhuber.
Incremental self-improvement for life-time multi-agent reinforcement
learning.
In Proceedings of the 4th International Conference of Simulation
of Adaptive Behaviros (SAB 4), pages 363-372. MIT Press, 1996.
Peter Stone
Fri Feb 27 18:45:43 EST 1998