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Using decision tree confidence factors for multi-agent control

Published: 01 May 1998 Publication History
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References

[1]
Minoru Asada, Eiji Uchibe, Shoichi Noda, Sukoya Tawaratsumida, and Koh Hosoda. Coordination of multiple behaviors acquired by vision-based reinforcement learning, In Proc, of IEEE/RSJ/GI International Conference on Intelligent Robots and Systems 1994 (IROS 19d), pages 917-924, 1994.
[2]
Mike Bowling, Peter Stone, and Manuela Veloso. Predictive memory for an inaccessible environment. In Pro. ceedings of the {ROS-96 Workshop on RoboCup, pages 28-34, Osaka, Japan, November 1996.
[3]
Andrew Garland and Richard Alterman. Multiagent learning through collective memory. In Adaptation, Co. evolution and Learning in Multiagent Systems: Papers from the 1996 AAA{ Spring Symposium, pages 33-38, Menlo Park,CA, March 1996. AAAI Press. AAAI Technical Report SS-96-01.
[4]
Jong-Hwan Kim, editor. Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea, November 1996.
[5]
ttiroaki Kitano, Yasuo Kuniyoshi, Itsuki Noda, Minoru Asada, Hitoshi Matsubara, and Eiichi Osawa. RoboCup: A challenge problem for AI. Air Magazine, 18(1):73-85, Spring 1997.
[6]
Hitoshi Matsubara, Itsuld Node, and Kazuo Hiraki. Learning of cooperative actions in multi-agent systems: a case study of pass play in soccer. In Adaptation, Co. evolution and Learning in Multiagent Systems: Papers from the 1996 AAAir Spring Symposium, pages 63--67, Menlo Park, CA, March 1996. AAAI Press. AAAI Technical Report SS-96-01.
[7]
Itstttd Nods and Hitoshi Matsubara. Soccer server and researches on multi-agent systems. In Proceedings of the IROS-96 Workshop on RoboCup, November 1996.
[8]
J. Ross Quinlan. C~.5: Programs for Machine Learn. ing. Morgan Kaufmann, San Mateo, CA, 1993.
[9]
Michael K. Sabots. Dynasim user guide.
[10]
Michael K. Sahota, Alan K. Mackworth, Rod A. Barman, and Stewart J. Kingdom Real-time control of soccer-playing robots using off-board vision: the dynamite testbed. In IEEE International Conference on Systems, Man, and Cybernetics, pages 3690-3663, 1995,
[11]
Randy Sargent, Bill Bailey, Carl Witty, and Anne Wright. Dynamic object capture using fast vision tracking. Air Magazine, 18(1):65-72, Spring 1997.
[12]
Peter Stone and Manuela Veloso. Beating a defender in robotic soccer: Memory-based learning of a continuous function. In David S. Touretzky, Michael C. Mozer, and Michael E. Hasselmo, editors, Advances in Neu. ral irnformation Processing Systems 8, pages 896-902, Cambridge, MA, 1996. MIT Press.
[13]
Peter Stone and Manuela Veloso. A layered approach to learning client behaviors in the RoboCup soccer server. Applied Artificial Intelligence, 12, 1998. In Press.
[14]
Peter Stone and Manuela Veloso. Towards collaborative and adversarial learning: A case study in robotic soccer. International Journal of Human. Computer Sys. tems, 48, 1998. In Press.
[15]
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.

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cover image ACM Conferences
AGENTS '98: Proceedings of the second international conference on Autonomous agents
May 1998
484 pages
ISBN:0897919831
DOI:10.1145/280765
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 May 1998

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AGENTS98
AGENTS98: 2nd International Conference on Autonomous Agents
May 10 - 13, 1998
Minnesota, Minneapolis, USA

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AGENTS '98 Paper Acceptance Rate 57 of 180 submissions, 32%;
Overall Acceptance Rate 182 of 599 submissions, 30%

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  • (2008)Improving the Efficiency of Low-Level Decision Making in Robosoccer Using Boosted SVMNew Challenges in Applied Intelligence Technologies10.1007/978-3-540-79355-7_6(55-64)Online publication date: 2008
  • (2007)Learning Strategies Based on Fuzzy Set Rules for the Ideal Opponent Model2007 International Conference on Emerging Technologies10.1109/ICET.2007.4516343(199-204)Online publication date: Nov-2007
  • (2007)An Efficient Goalie Strategy Using Twin Hidden Markov Models7th IEEE International Conference on Computer and Information Technology (CIT 2007)10.1109/CIT.2007.175(157-164)Online publication date: Oct-2007
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  • (2006)Assessing the value of future and present options in real-time planningProceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence10.1007/11874850_56(522-531)Online publication date: 23-Oct-2006
  • (2006)Emergent Cooperation in RoboCup: A ReviewRoboCup 2005: Robot Soccer World Cup IX10.1007/11780519_48(512-520)Online publication date: 2006
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  • (2005)Survivability of a distributed multi-agent application - a performance control perspectiveIEEE 2nd Symposium on Multi-Agent Security and Survivability, 2005.10.1109/MASSUR.2005.1507044(21-30)Online publication date: 2005
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