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AAAI
2010
13 years 9 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»
13 years 11 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»
13 years 2 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
IJCAI
2001
13 years 9 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»
13 years 9 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