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ROBOCUP
2001
Springer
96views Robotics» more  ROBOCUP 2001»
15 years 4 months ago
Strategy Learning for a Team in Adversary Environments
Team strategy acquisition is one of the most important issues of multiagent systems, especially in an adversary environment. RoboCup has been providing such an environment for AI a...
Yasutake Takahashi, Takashi Tamura, Minoru Asada
94
Voted
AI
1999
Springer
14 years 11 months ago
Introspective Multistrategy Learning: On the Construction of Learning Strategies
A central problem in multistrategy learning systems is the selection and sequencing of machine learning algorithms for particular situations. This is typically done by the system ...
Michael T. Cox, Ashwin Ram
88
Voted
HCI
2007
15 years 1 months ago
A Closed-Loop Adaptive System for Command and Control
On Navy ships, technological developments enable crews to work more efficiently and effectively. However, in such complex, autonomous, and information-rich environments a competiti...
Tjerk de Greef, Henryk Arciszewski
MKTSCI
2008
68views more  MKTSCI 2008»
14 years 11 months ago
Supermarket Pricing Strategies
Most supermarket firms choose to position themselves by offering either "Every Day Low Prices" (EDLP) across several items or offering temporary price reductions (promot...
Paul B. Ellickson, Sanjog Misra
ROBOCUP
2004
Springer
114views Robotics» more  ROBOCUP 2004»
15 years 5 months ago
Modular Learning System and Scheduling for Behavior Acquisition in Multi-agent Environment
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since othe...
Yasutake Takahashi, Kazuhiro Edazawa, Minoru Asada