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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.13 n.1 México Jul./Sep. 2009

 

Artículos

 

AsistO: A Qualitative MDP–based Recommender System for Power Plant Operation

 

AsistO: Un Sistema de Recomendaciones basado en MDPs Cualitativos para la Operación de Plantas Generadoras

 

Alberto Reyes1, L. Enrique Sucar2 and Eduardo F. Morales2

 

1 Instituto de Investigaciones Eléctricas; Av. Reforma 113, Palmira, Cuernavaca, Morelos, 62490, México; areyes@iie.org.mx

2 INAOE; Luis Enrique Erro 1, Sta. Ma. Tonantzintla, Puebla 72840, México; esucar@inaoep.mx , emorales@inaoep.mx

 

Article received on July 15, 2008
Accepted on April 03, 2009

 

Abstract

This paper proposes a novel and practical model–based learning approach with iterative refinement for solving continuous (and hybrid) Markov decision processes. Initially, an approximate model is learned using conventional sampling methods and solved to obtain a policy. Iteratively, the approximate model is refined using variance in the utility values as partition criterion. In the learning phase, initial reward and transition functions are obtained by sampling the state–action space. The samples are used to induce a decision tree predicting reward values from which an initial partition of the state space is built. The samples are also used to induce a factored MDP. The state abstraction is then refined by splitting states only where the split is locally important. The main contributions of this paper are the use of sampling to construct an abstraction, and a local refinement process of the state abstraction based on utility variance. The proposed technique was tested in AsistO, an intelligent recommender system for power plant operation, where we solved two versions of a complex hybrid continuous–discrete problem. We show how our technique approximates a solution even in cases where standard methods explode computationally.

Keywords: Recommender systems, power plants, Markov decision processes, abstractions.

 

Resumen

Este artículo propone una técnica novedosa y práctica de aprendizaje basada en modelos con refinamiento iterativo para resolver procesos de decisión de Markov (MDPs) continuos. Inicialmente, se aprende un modelo aproximado usando métodos de muestreo convencionales, el cual se resuelve para obtener una política. Iterativamente, el modelo aproximado se refina con base en la varianza de los valores de la utilidad esperada. En la fase de aprendizaje, se obtienen las funciones de recompensa inmediata y de transición mediante muestras del tipo estado–acción. Éstas primero se usan para inducir un árbol de decisión que predice los valores de recompensa y a partir del cual se construye una partición inicial del espacio de estados. Posteriormente, las muestras también se usan para inducir un MDP factorizado. Finalmente, la abstracción de espacio de estados resultante se refina dividiendo aquellos estados donde pueda haber cambios en la política. Las contribuciones principales de este trabajo son el uso de datos para construir una abstracción inicial, y el proceso de refinamiento local basado en la varianza de la utilidad. La técnica propuesta fue probada en AsistO, un sistema inteligente de recomendaciones para la operación de plantas generadoras de electricidad, donde resolvimos dos versiones de un problema complejo con variables híbridas continuas y discretas. Aquí mostramos como nuestra técnica aproxima una solución aun en casos donde los métodos estándar explotan computacionalmente.

Palabras clave: Sistemas de recomendaciones, plantas generadoras, procesos de decisión de Markov, abstracciones.

 

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Acknowledgments

This work was supported jointly by the Instituto de Investigaciones Eléctricas, Mexico and CONACYT Project No. 47968.

 

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