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» Ranking policies in discrete Markov decision processes
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GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
14 years 1 months ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
WISE
2002
Springer
14 years 27 days ago
An MDP-based Peer-to-Peer Search Server Network
A distributed search system consists of a large number of autonomous search servers logically connected in a peerto-peer network. Each search server maintains a local index of a c...
Yipeng Shen, Dik Lun Lee
CCE
2004
13 years 7 months ago
Dynamic programming in a heuristically confined state space: a stochastic resource-constrained project scheduling application
The Resource-Constrained Project Scheduling Problem(RCPSP) is a significant challenge in highly regulated industries, such as pharmaceuticals and agrochemicals, where a large numb...
Jaein Choi, Matthew J. Realff, Jay H. Lee
ATAL
2009
Springer
14 years 2 months ago
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this fa...
Jilles Steeve Dibangoye, Abdel-Illah Mouaddib, Bra...
AAAI
2006
13 years 9 months ago
Learning Basis Functions in Hybrid Domains
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Branislav Kveton, Milos Hauskrecht