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FLAIRS
2004
15 years 8 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
EUROPLOP
2006
15 years 8 months ago
Migrating to e-Learning in Secondary Education
Traditional face-to-face instruction in secondary education has been tested for hundreds of years now and has proven successful, but after the tremendous expansion of computer and...
Michalis Hadjisimou, Aimilia Tzanavari
NIPS
2004
15 years 8 months ago
Learning first-order Markov models for control
First-order Markov models have been successfully applied to many problems, for example in modeling sequential data using Markov chains, and modeling control problems using the Mar...
Pieter Abbeel, Andrew Y. Ng
UAI
2000
15 years 8 months ago
Tractable Bayesian Learning of Tree Belief Networks
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tracta...
Marina Meila, Tommi Jaakkola
IJCAI
1993
15 years 8 months ago
Learning Finite Automata Using Local Distinguishing Experiments
One of the open problems listed in Rivest and Schapire, 1989] is whether and how that the copies of L in their algorithm can be combined into one for better performance. This pape...
Wei-Mein Shen