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» RoboCup: Today and Tomorrow - What we have learned
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ROBOCUP
1999
Springer
129views Robotics» more  ROBOCUP 1999»
13 years 10 months ago
The Ulm Sparrows 99
In RoboCup-98, sparrows team worked hard just to get both a simulation and a middle size robot team to work and to successfully participate in a major tournament. For this year, we...
Stefan Sablatnög, Stefan Enderle, Mark Dettin...
AI
2004
Springer
13 years 11 months ago
Multi-attribute Decision Making in a Complex Multiagent Environment Using Reinforcement Learning with Selective Perception
Abstract. Choosing between multiple alternative tasks is a hard problem for agents evolving in an uncertain real-time multiagent environment. An example of such environment is the ...
Sébastien Paquet, Nicolas Bernier, Brahim C...
ROBOCUP
2005
Springer
99views Robotics» more  ROBOCUP 2005»
13 years 11 months ago
Keepaway Soccer: From Machine Learning Testbed to Benchmark
Keepaway soccer has been previously put forth as a testbed for machine learning. Although multiple researchers have used it successfully for machine learning experiments, doing so ...
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, ...
AAAI
2006
13 years 7 months ago
Educational Robotics in Brooklyn
We describe a number of efforts to engage university students with robotics through teaching and outreach. Teaching runs the gamut from undergraduate introductory computer science...
Elizabeth Sklar, Simon Parsons, M. Q. Azhar, Valer...
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
13 years 11 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu