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» Algorithm Selection using Reinforcement Learning
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NIPS
2004
15 years 4 months ago
Responding to Modalities with Different Latencies
Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of reinforcement learning how different s...
Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, ...
IJON
2006
134views more  IJON 2006»
15 years 3 months ago
A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems
A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbi...
Mu-Chun Su, Chien-Hsing Chou, Eugene Lai, Jonathan...
FSKD
2007
Springer
98views Fuzzy Logic» more  FSKD 2007»
15 years 9 months ago
Learning Selective Averaged One-Dependence Estimators for Probability Estimation
Naïve Bayes is a well-known effective and efficient classification algorithm, but its probability estimation performance is poor. Averaged One-Dependence Estimators, simply AODE,...
Qing Wang, Chuan-hua Zhou, Jiankui Guo
ICCBR
2005
Springer
15 years 8 months ago
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time ...
David W. Aha, Matthew Molineaux, Marc J. V. Ponsen
JAIR
2008
148views more  JAIR 2008»
15 years 3 months ago
Learning Partially Observable Deterministic Action Models
We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the ac...
Eyal Amir, Allen Chang