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» Robustness in Probabilistic Temporal Planning
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CP
2005
Springer
14 years 1 months ago
Solving Simple Planning Problems with More Inference and No Search
Many problems used in AI planning including Blocks, Logistics, Gripper, Satellite, and others lack the interactions that characterize puzzles and can be solved nonoptimally in low...
Vincent Vidal, Hector Geffner
TROB
2010
129views more  TROB 2010»
13 years 5 months ago
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
—Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation,...
Lars Blackmore, Masahiro Ono, Askar Bektassov, Bri...
JAIR
2006
157views more  JAIR 2006»
13 years 7 months ago
Decision-Theoretic Planning with non-Markovian Rewards
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic...
Sylvie Thiébaux, Charles Gretton, John K. S...
TCSV
2011
13 years 2 months ago
Integrating Spatio-Temporal Context With Multiview Representation for Object Recognition in Visual Surveillance
—We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists...
Xiaobai Liu, Liang Lin, Shuicheng Yan, Hai Jin, We...
ICPR
2008
IEEE
14 years 1 months ago
Tracking human body by using particle filter Gaussian process Markov-switching model
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...
Jing Wang, Hong Man, Yafeng Yin