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FUZZIEEE
2007
IEEE
15 years 4 months ago
Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms
— Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzyvalued data. These algorithms are efficient when the observation e...
Luciano Sánchez, José Otero
HRI
2010
ACM
14 years 11 months ago
The hesitation of a robot: a delay in its motion increases learning efficiency and impresses humans as teachable
If robots learn new actions through human-robot interaction, it is important that the robots can utilize rewards as well as instructions to reduce humans' efforts. Additionall...
Kazuaki Tanaka, Motoyuki Ozeki, Natsuki Oka
CONNECTION
2006
101views more  CONNECTION 2006»
14 years 10 months ago
Learning acceptable windows of contingency
By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world...
Kevin Gold, Brian Scassellati
IJCNN
2008
IEEE
15 years 4 months ago
Uncertainty propagation for quality assurance in Reinforcement Learning
— In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking in...
Daniel Schneegaß, Steffen Udluft, Thomas Mar...
ISIPTA
2005
IEEE
162views Mathematics» more  ISIPTA 2005»
15 years 3 months ago
Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model
A new model for learning from multinomial data has recently been developed, giving predictive inferences in the form of lower and upper probabilities for a future observation. Apa...
Frank P. A. Coolen, Thomas Augustin