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ATAL
2006
Springer
14 years 12 months ago
Selecting informative actions improves cooperative multiagent learning
In concurrent cooperative multiagent learning, each agent simultaneously learns to improve the overall performance of the team, with no direct control over the actions chosen by i...
Liviu Panait, Sean Luke
GECCO
2008
Springer
126views Optimization» more  GECCO 2008»
14 years 11 months ago
Swarm intelligence in e-learning: a learning object sequencing agent based on competencies
In e-learning initiatives content creators are usually required to arrange a set of learning resources in order to present them in a comprehensive way to the learner. Course mater...
Luis de Marcos, José-Javier Martínez...
SIGIR
2008
ACM
14 years 9 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 1 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
KDD
2005
ACM
143views Data Mining» more  KDD 2005»
15 years 10 months ago
SVM selective sampling for ranking with application to data retrieval
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vect...
Hwanjo Yu