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

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dc.creatorDambreville, Frederic
dc.date2004-08-11
dc.date.accessioned2026-07-07T06:30:42Z
dc.date.available2026-07-07T06:30:42Z
dc.descriptionIn 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.
dc.descriptionWriting date : July 30 2004 Submitted to the European Journal of Operation Research
dc.identifierhttps://arxiv.org/abs/math/0408146
dc.identifierhttp://arxiv.org/abs/math/0408146
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/98406
dc.subjectGeneral Mathematics
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.titleLearning a Machine for the Decision in a Partially Observable Markov Universe
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