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ESWA
2008
169views more  ESWA 2008»
15 years 6 months ago
Predicting opponent's moves in electronic negotiations using neural networks
Electronic negotiation experiments provide a rich source of information about relationships between the negotiators, their individual actions, and the negotiation dynami...
Réal Carbonneau, Gregory E. Kersten, Rustam...
AUSAI
1999
Springer
15 years 11 months ago
Q-Learning in Continuous State and Action Spaces
Abstract. Q-learning can be used to learn a control policy that maximises a scalar reward through interaction with the environment. Qlearning is commonly applied to problems with d...
Chris Gaskett, David Wettergreen, Alexander Zelins...
CEC
2008
IEEE
15 years 8 months ago
Learning defect classifiers for visual inspection images by neuro-evolution using weakly labelled training data
This article presents results from experiments where a detector for defects in visual inspection images was learned from scratch by EANT2, a method for evolutionary reinforcement l...
Nils T. Siebel, Gerald Sommer
AI50
2006
15 years 10 months ago
What Can AI Get from Neuroscience?
The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. AI researchers trying to imitate its re...
Steve M. Potter
NIPS
1993
15 years 8 months ago
Credit Assignment through Time: Alternatives to Backpropagation
Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networ...
Yoshua Bengio, Paolo Frasconi