Learning a Machine for the Decision in a Partially Observable Markov Universe

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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.
Writing date : July 30 2004 Submitted to the European Journal of Operation Research

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General Mathematics, Artificial Intelligence, Machine Learning

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http://salesiana.dossiersoluciones.com/handle/123456789/98406

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0408146
 http://arxiv.org/abs/math/0408146

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arXiv