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AAAI
2010
15 years 4 months ago
Symbolic Dynamic Programming for First-order POMDPs
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
Scott Sanner, Kristian Kersting
EUROGP
2007
Springer
126views Optimization» more  EUROGP 2007»
15 years 7 months ago
Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach
The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the ...
Michal Lemczyk, Malcolm I. Heywood
TEC
2011
134views more  TEC 2011»
14 years 10 months ago
Differential Evolution: A Survey of the State-of-the-Art
—Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational step...
Swagatam Das, Ponnuthurai Nagaratnam Suganthan
133
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IJCAI
2001
15 years 4 months ago
Symbolic Dynamic Programming for First-Order MDPs
We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the ...
Craig Boutilier, Raymond Reiter, Bob Price
ATMOS
2007
177views Optimization» more  ATMOS 2007»
15 years 4 months ago
Approximate dynamic programming for rail operations
Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Warren B. Powell, Belgacem Bouzaïene-Ayari