Learning stationary temporal probabilistic networks
Conference Paper, Proceedings of Conference on Automated Learning and Discovery (CONALD '98), June, 1998
Abstract
The paper describes a method for learning representations of partially observable Markov decision processes in the form of temporal probabilistic networks, which can subsequently be used by robotic agents for action planning and policy determination. A solution is provided to the problem of enforcing stationarity of the learned Markov model. Several preliminary experiments are described that con rm the validity of the solution.
BibTeX
@conference{Nikovski-1998-16551,author = {Daniel Nikovski},
title = {Learning stationary temporal probabilistic networks},
booktitle = {Proceedings of Conference on Automated Learning and Discovery (CONALD '98)},
year = {1998},
month = {June},
}
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