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CSL
2007
Springer
13 years 7 months ago
Partially observable Markov decision processes for spoken dialog systems
In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence ...
Jason D. Williams, Steve Young
DAM
2008
61views more  DAM 2008»
13 years 7 months ago
Scheduling malleable tasks with interdependent processing rates: Comments and observations
In this short paper we examine the problem of scheduling malleable tasks on parallel processors. One of the main aims of the paper is to present a simple complexity interpretation...
Edmund K. Burke, Moshe Dror, James B. Orlin
IROS
2008
IEEE
211views Robotics» more  IROS 2008»
14 years 1 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
DSN
2006
IEEE
14 years 1 months ago
Automatic Recovery Using Bounded Partially Observable Markov Decision Processes
This paper provides a technique, based on partially observable Markov decision processes (POMDPs), for building automatic recovery controllers to guide distributed system recovery...
Kaustubh R. Joshi, William H. Sanders, Matti A. Hi...
HCI
2009
13 years 5 months ago
Partially Observable Markov Decision Process (POMDP) Technologies for Sign Language Based Human-Computer Interaction
Sign language (SL) recognition modules in human-computer interaction systems need to be both fast and reliable. In cases where multiple sets of features are extracted from the SL d...
Sylvie C. W. Ong, David Hsu, Wee Sun Lee, Hanna Ku...