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ICML
2002
IEEE
16 years 1 months ago
Discovering Hierarchy in Reinforcement Learning with HEXQ
An open problem in reinforcement learning is discovering hierarchical structure. HEXQ, an algorithm which automatically attempts to decompose and solve a model-free factored MDP h...
Bernhard Hengst
111
Voted
ATAL
2009
Springer
15 years 7 months ago
Evaluating hybrid constraint tightening for scheduling agents
Hybrid Scheduling Problems (HSPs) combine temporal and finite-domain variables via hybrid constraints that dictate that specific bounds on temporal constraints rely on assignments...
James C. Boerkoel Jr., Edmund H. Durfee
GECCO
2005
Springer
15 years 6 months ago
Probabilistic distribution models for EDA-based GP
This paper proposes a novel technique for a program evolution based on probabilistic models. In the proposed method, two probabilistic distribution models with probabilistic depen...
Kohsuke Yanai, Hitoshi Iba
84
Voted
AIPS
2000
15 years 2 months ago
Distance-Based Goal-Ordering Heuristics for Graphplan
We will discuss the shortcomings of known variable and value ordering strategies for Graphplan's backward search phase, and propose a novel strategy that is based on a notion...
Subbarao Kambhampati, Romeo Sanchez Nigenda
108
Voted
AAAI
2007
15 years 3 months ago
Learning Graphical Model Structure Using L1-Regularization Paths
Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
Mark W. Schmidt, Alexandru Niculescu-Mizil, Kevin ...