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96
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IROS
2008
IEEE
211views Robotics» more  IROS 2008»
15 years 4 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
AAAI
2006
14 years 11 months ago
Learning Partially Observable Action Models: Efficient Algorithms
We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
Dafna Shahaf, Allen Chang, Eyal Amir
ATAL
2007
Springer
15 years 1 months ago
On discovery and learning of models with predictive representations of state for agents with continuous actions and observations
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly focused on the case of discrete observations and actions. The theory of discre...
David Wingate, Satinder P. Singh
DEDS
2008
87views more  DEDS 2008»
14 years 9 months ago
On-Line Monitoring of Large Petri Net Models Under Partial Observation
This paper deals with the on-line monitoring of large systems modeled as Petri Nets under partial observation. The plant observation is given by a subset of transitions whose occu...
George Jiroveanu, René K. Boel, Behzad Bord...
87
Voted
ICIP
2003
IEEE
15 years 11 months ago
An optical flow probabilistic observation model for tracking
In this paper, we define an observation model based on optical flow information to track objects using particle filter algorithms. Although the optical flow information enables us...
Antonio Garrido Carrillo, José M. Fuertes, ...