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164
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CIMCA
2008
IEEE
15 years 11 months ago
2008
IEEE
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
153
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ICTAI
2009
IEEE
15 years 2 months ago
2009
IEEE
We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Miss...
156
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GECCO
2006
Springer
15 years 8 months ago
2006
Springer
Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search...
134
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CVPR
1999
IEEE
16 years 7 months ago
1999
IEEE
We o er a simple paradigm for tting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done ...
143
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IPSN
2010
Springer
15 years 11 months ago
2010
Springer
We consider the problem of selecting an optimal set of sensors, as determined, for example, by the predictive accuracy of the resulting sensor network. Given an underlying metric ...