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ICML
2006
IEEE
16 years 7 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
IJCAI
2003
15 years 8 months ago
Amalgams of Constraint Satisfaction Problems
Many of standard practical techniques of solving constraint satisfaction problems use various decomposition methods to represent a problem as a combination of smaller ones. We stu...
Andrei A. Bulatov, Evgeny S. Skvortsov
PODC
2009
ACM
16 years 7 months ago
Distributed and parallel algorithms for weighted vertex cover and other covering problems
The paper presents distributed and parallel -approximation algorithms for covering problems, where is the maximum number of variables on which any constraint depends (for example...
Christos Koufogiannakis, Neal E. Young
SARA
2009
Springer
16 years 1 months ago
Common Subexpressions in Constraint Models of Planning Problems
Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective cons...
Andrea Rendl, Ian Miguel, Ian P. Gent, Peter Grego...
GECCO
2007
Springer
193views Optimization» more  GECCO 2007»
16 years 24 days ago
Approximating covering problems by randomized search heuristics using multi-objective models
The main aim of randomized search heuristics is to produce good approximations of optimal solutions within a small amount of time. In contrast to numerous experimental results, th...
Tobias Friedrich, Nils Hebbinghaus, Frank Neumann,...