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ICML
1994
IEEE
15 years 5 months ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
AIPS
2008
15 years 4 months ago
Bounded-Parameter Partially Observable Markov Decision Processes
The POMDP is considered as a powerful model for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model precisely the real-...
Yaodong Ni, Zhi-Qiang Liu
PE
2010
Springer
124views Optimization» more  PE 2010»
14 years 8 months ago
High-level approach to modeling of observed system behavior
Current computer systems and communication networks tend to be highly complex, and they typically hide their internal structure from their users. Thus, for selected aspects of cap...
Thomas Begin, Alexandre Brandwajn, Bruno Baynat, B...
ICASSP
2011
IEEE
14 years 5 months ago
On selecting the hyperparameters of the DPM models for the density estimation of observation errors
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
MM
2006
ACM
197views Multimedia» more  MM 2006»
15 years 8 months ago
Virtual observers in a mobile surveillance system
Conventional wide-area video surveillance systems use a network of fixed cameras positioned close to locations of interest. We describe an alternative and flexible approach to w...
Stewart Greenhill, Svetha Venkatesh