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RSS
2007
176views Robotics» more  RSS 2007»
13 years 9 months ago
Active Policy Learning for Robot Planning and Exploration under Uncertainty
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
FSS
2008
87views more  FSS 2008»
13 years 7 months ago
Representing parametric probabilistic models tainted with imprecision
Numerical possibility theory, belief function have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in unc...
Cédric Baudrit, Didier Dubois, Nathalie Per...
ICASSP
2009
IEEE
13 years 5 months ago
Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior
In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches ...
Shoko Araki, Tomohiro Nakatani, Hiroshi Sawada, Sh...
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
14 years 1 months ago
A multi-objective genetic algorithm for robust design optimization
Real-world multi-objective engineering design optimization problems often have parameters with uncontrollable variations. The aim of solving such problems is to obtain solutions t...
Mian Li, Shapour Azarm, Vikrant Aute
ISIPTA
2003
IEEE
111views Mathematics» more  ISIPTA 2003»
14 years 26 days ago
The DecideIT Decision Tool
The nature of much information available to decision makers is vague and imprecise, be it information for human managers in organisations or for process agents in a distributed co...
Mats Danielson, Love Ekenberg, Jim Johansson, Aron...