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140
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COLT
2006
Springer
15 years 7 months ago
Online Learning with Constraints
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The...
Shie Mannor, John N. Tsitsiklis
145
Voted
GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 7 months ago
Dominance learning in diploid genetic algorithms for dynamic optimization problems
This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus i...
Shengxiang Yang
111
Voted
AIPS
2008
15 years 6 months ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein
118
Voted
NIPS
2007
15 years 5 months ago
An online Hebbian learning rule that performs Independent Component Analysis
Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but o...
Claudia Clopath, André Longtin, Wulfram Ger...
115
Voted
AIPS
2006
15 years 5 months ago
Learning to Do HTN Planning
We describe HDL, an algorithm that learns HTN domain descriptions by examining plan traces produced by an expert problem-solver. Prior work on learning HTN methods requires that a...
Okhtay Ilghami, Dana S. Nau, Héctor Mu&ntil...