Learning a Machine for the Decision in a Partially Observable Markov Universe
General Mathematics
2007-05-23 v1 Artificial Intelligence
Machine Learning
Abstract
In this paper, we are interested in optimal decisions in a partially observable Markov universe. Our viewpoint departs from the dynamic programming viewpoint: we are directly approximating an optimal strategic tree depending on the observation. This approximation is made by means of a parameterized probabilistic law. In this paper, a particular family of hidden Markov models, with input and output, is considered as a learning framework. A method for optimizing the parameters of these HMMs is proposed and applied. This optimization method is based on the cross-entropic principle.
Cite
@article{arxiv.math/0408146,
title = {Learning a Machine for the Decision in a Partially Observable Markov Universe},
author = {Frederic Dambreville},
journal= {arXiv preprint arXiv:math/0408146},
year = {2007}
}
Comments
Writing date : July 30 2004 Submitted to the European Journal of Operation Research