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PRICAI
2000
Springer
15 years 3 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst
ICML
2010
IEEE
15 years 20 days ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
AI
1999
Springer
14 years 11 months ago
Learning by Discovering Concept Hierarchies
We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and th...
Blaz Zupan, Marko Bohanec, Janez Demsar, Ivan Brat...
ENTCS
2010
95views more  ENTCS 2010»
14 years 9 months ago
Quadtrees as an Abstract Domain
s as an Abstract Domain Jacob M. Howe1,4 Dept of Computing, City University London, UK Andy King1,3,5 School of Computing, University of Kent, Canterbury, UK Charles Lawrence-Jones...
Jacob M. Howe, Andy King, Charles Lawrence-Jones
TFS
2008
94views more  TFS 2008»
14 years 11 months ago
Hierarchical Fuzzy CMAC for Nonlinear Systems Modeling
Abstract--Since the fuzzy cerebellar model articulation controller (FCMAC) uses linguistic variables, it is highly intuitive and easily comprehended. Despite the FCMAC's good ...
Wen Yu, Floriberto Ortiz Rodriguez, Marco A. Moren...