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IJRR
2010
162views more  IJRR 2010»
14 years 8 months ago
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
CVIU
2006
76views more  CVIU 2006»
14 years 9 months ago
Homeostatic image perception: An artificial system
This paper describes how a visual system can automatically define features of interest from the observation of a large enough number of natural images. The principle complements t...
Thomas Feldman, Laurent Younes
AIPS
2007
15 years 4 days ago
Learning to Plan Using Harmonic Analysis of Diffusion Models
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
IAT
2009
IEEE
15 years 4 months ago
Introducing Communication in Dis-POMDPs with Finite State Machines
Distributed Partially Observable Markov Decision Problems (DisPOMDPs) are emerging as a popular approach for modeling sequential decision making in teams operating under uncertain...
Yuki Iwanari, Makoto Tasaki, Makoto Yokoo, Atsushi...
ICPR
2008
IEEE
15 years 4 months ago
Optimal feature weighting for the continuous HMM
We propose new Continuous Hidden Markov Model (CHMM) structure that integrates feature weighting component. We assume that each feature vector could include different subsets of f...
Oualid Missaoui, Hichem Frigui