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ICML
2005
IEEE
16 years 4 months ago
Recycling data for multi-agent learning
Learning agents can improve performance cooperating with other agents, particularly learning agents forming a committee outperform individual agents. This "ensemble effect&qu...
Santiago Ontañón, Enric Plaza
ESANN
2001
15 years 5 months ago
Motor control and movement optimization learned by combining auto-imitative and genetic algorithms
In sensorimotor behaviour often a great movement execution variability is combined with a relatively low error in reaching the intended goal. This phenomenon can especially be obse...
Karl-Theodor Kalveram, Ulrich Nakte
131
Voted
LEGE
2003
94views Education» more  LEGE 2003»
15 years 5 months ago
Dynamic Learning Agents and Enhanced Presence on the Grid
Human Learning on the Grid will be based on the synergies between advanced software and Human agents. These synergies will be possible to the extent that conversational protocols ...
Stefano A. Cerri, Marc Eisenstadt, Clement Jonquet
ML
2008
ACM
15 years 4 months ago
Margin-based first-order rule learning
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
Ulrich Rückert, Stefan Kramer
CE
2004
153views more  CE 2004»
15 years 3 months ago
Let's get physical: The learning benefits of interacting in digitally augmented physical spaces
Much computer-based learning is largely passive, based primarily on task-based, exercise-driven interactions. We argue that computers have greater potential for promoting more act...
Sara Price, Yvonne Rogers