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ETAI
2000
84views more  ETAI 2000»
15 years 6 months ago
Learning Stochastic Logic Programs
Stochastic logic programs combine ideas from probabilistic grammars with the expressive power of definite clause logic; as such they can be considered as an extension of probabili...
Stephen Muggleton
MDM
2009
Springer
201views Communications» more  MDM 2009»
16 years 1 months ago
OntoMobiLe: A Generic Ontology-Centric Service-Oriented Architecture for Mobile Learning
Creation of pedagogical learning models to handle the specificity of mobile learning and the inherent constraints of mobile devices is a fundamental challenge in mobile learning. ...
Keng Y. Yee, Wee Tiong Ang, Flora S. Tsai, Rajaram...
ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
16 years 1 months ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
CIG
2005
IEEE
16 years 15 days ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 11 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein