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ECML
2005
Springer
13 years 10 months ago
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
Masoumeh T. Izadi, Doina Precup
ANSS
1996
IEEE
13 years 9 months ago
Computation of the Asymptotic Bias and Variance for Simulation of Markov Reward Models
The asymptotic bias and variance are important determinants of the quality of a simulation run. In particular, the asymptotic bias can be used to approximate the bias introduced b...
Aad P. A. van Moorsel, Latha A. Kant, William H. S...
UAI
1998
13 years 6 months ago
Hierarchical Solution of Markov Decision Processes using Macro-actions
tigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-...
Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kae...
ICCV
2003
IEEE
14 years 7 months ago
Minimum Risk Distance Measure for Object Recognition
Recently, the optimal distance measure for a given object discrimination task under the nearest neighbor framework was derived [1]. For ease of implementation and efficiency consi...
Shyjan Mahamud, Martial Hebert
STOC
1997
ACM
125views Algorithms» more  STOC 1997»
13 years 9 months ago
An Interruptible Algorithm for Perfect Sampling via Markov Chains
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an...
James Allen Fill