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ICML
2006
IEEE
16 years 3 months ago
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
Inference in Markov Decision Processes has recently received interest as a means to infer goals of an observed action, policy recognition, and also as a tool to compute policies. ...
Marc Toussaint, Amos J. Storkey
GECCO
2004
Springer
123views Optimization» more  GECCO 2004»
15 years 8 months ago
Heuristic Methods for Solving Euclidean Non-uniform Steiner Tree Problems
In this paper, we consider a variation of the Euclidean Steiner Tree Problem in which the space underlying the set of nodes has a specified non-uniform cost structure. This proble...
Ian Frommer, Bruce L. Golden, Guruprasad Pundoor
MOC
2000
105views more  MOC 2000»
15 years 2 months ago
On the iteratively regularized Gauss-Newton method for solving nonlinear ill-posed problems
The iteratively regularized Gauss-Newton method is applied to compute the stable solutions to nonlinear ill-posed problems F (x) = y when the data y is given approximately by y wit...
Jin Qi-nian
GECCO
2007
Springer
300views Optimization» more  GECCO 2007»
15 years 9 months ago
Methodology to select solutions from the pareto-optimal set: a comparative study
The resolution of a Multi-Objective Optimization Problem (MOOP) does not end when the Pareto-optimal set is found. In real problems, a single solution must be selected. Ideally, t...
José C. Ferreira, Carlos M. Fonseca, Ant&oa...
AAAI
2000
15 years 4 months ago
Solving Combinatorial Auctions Using Stochastic Local Search
Combinatorial auctions (CAs) have emerged as an important model in economics and show promise as a useful tool for tackling resource allocation in AI. Unfortunately, winner determ...
Holger H. Hoos, Craig Boutilier