BayesChess: A computer chess program based on Bayesian networks

In this paper we introduce a chess program able to adapt its game strategy to its opponent, as well as to adapt the evaluation function that guides the search process according to its playing experience. The adaptive and learning abilities have been implemented through Bayesian networks. We show how the program learns through an experiment consisting on a series of games that point out that the results improve after the learning stage

BayesChess: programa de ajedrez adaptativo basado en redes bayesianas

En este trabajo presentamos un programa de ajedrez capaz de adaptar su estrategia al usuario al que se enfrenta y de refinar la función de evaluación que guía el proceso de búsqueda en base a su propia experiencia de juego. La capacidad adaptativa y de aprendizaje se ha implementado mediante redes bayesianas. Mostramos el proceso de aprendizaje del programa mediante una experimentación consistente en una serie de partidas que evidencian una mejora en los resultados después de la fase de aprendizaje

Approximate Probability Propagation with Mixtures of Truncated Exponentials*

Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working with hybrid Bayesian networks. One of the features of the MTE model is that standard propagation algorithms can be used. However, the complexity of the process is too high and therefore approximate methods, which tradeoff complexity for accuracy, become necessary. In this paper we propose an approximate propagation algorithm for MTE networks which is based on the Penniless propagation method already known for discrete variables. We also consider how to use Markov Chain Monte Carlo to carry out the probability propagation. The performance of the proposed methods is analysed in a series of experiments with random networks

Dynamic Importance Sampling in Bayesian Networks Based on Probability Trees

In this paper we introduce a new dynamic importance sampling propagation algorithm for Bayesian networks. Importance sampling is based on using an auxiliary sampling distribution from which a set of con gurations of the variables in the network is drawn, and the performance of the algorithm depends on the variance of the weights associated with the simulated con gurations. The basic idea of dynamic importance sampling is to use the simulation of a con guration to modify the sampling distribution in order to improve its quality and so reducing the variance of the future weights. The paper shows that this can be achieved with a low computational effort. The experiments carried out show that the nal results can be very good even in the case that the initial sampling distribution is far away from the optimum

Answering queries in hybrid Bayesian networks using importance sampling

In this paper we propose an algorithm for answering queries in hybrid Bayesian networks where the underlying probability distribution is of class MTE (mixture of truncated exponentials). The algorithm is based on importance sampling simulation. We show how, like existing importance sampling algorithms for discrete networks, it is able to provide answers to multiple queries simultaneously using a single sample. The behaviour of the new algorithm is experimentally tested and compared with previous methods existing in the literature

Comments on: Hybrid semiparametric Bayesian networks

The authors present an interesting work that extends their previous contribution on semiparametric Bayesian networks to a more general class of models, namely hybrid Bayesian networks, in which discrete (or categorical) and continuous variables coexist

Learning naive Bayes regression models with missing data using mixtures of truncated exponentials

In the last years, mixtures of truncated exponentials (MTEs) have received much attention within the context of probabilistic graphical models, as they provide a framework for hybrid Bayesian networks which is compatible with standard inference algorithms and no restriction on the structure of the network is considered. Recently, MTEs have also been successfully applied to regression problems in which the underlying network structure is a na ̈ıve Bayes or a TAN. However, the algorithms described so far in the literature operate over complete databases. In this paper we propose an iterative algorithm for constructing na ̈ıve Bayes regression models from incomplete databases. It is based on a variation of the data augmentation method in which the missing values of the explanatory variables are filled by simulating from their posterior distributions, while the missing values of the response variable are generated from its conditional expectation given the explanatory variables. We illustrate through a set of experiments with various databases that the proposed algorithm behaves reasonably well

LEARNING BAYESIAN NETWORKS FOR REGRESSION FROM INCOMPLETE DATABASES*

In this paper we address the problem of inducing Bayesian network models for regression from incomplete databases. We use mixtures of truncated exponentials (MTEs) to represent the joint distribution in the induced networks. We consider two particular Bayesian network structures, the so-called na¨ıve Bayes and TAN, which have been successfully used as regression models when learning from complete data. We propose an iterative procedure for inducing the models, based on a variation of the data augmentation method in which the missing values of the explanatory variables are filled by simulating from their posterior distributions, while the missing values of the response variable are generated using the conditional expectation of the response given the explanatory variables. We also consider the refinement of the regression models by using variable selection and bias reduction. We illustrate through a set of experiments with various databases the performance of the proposed algorithms

Learning hybrid Bayesian networks using mixtures of truncated exponentials

In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The structure of the network is obtained by searching over the space of candidate networks using optimisation methods. The conditional densities are estimated by means of Gaussian kernel densities that afterwards are approximated by MTEs, so that the resulting network is appropriate for using standard algorithms for probabilistic reasoning. The behaviour of the proposed algorithm is tested using a set of real-world and arti cially generated databases

Metodología para el análisis de relevancia de indicadores de rendimiento en educación superior

En este artículo proponemos una metodología para el análisis de relevancia de indicadores de rendimiento basada en el uso de redes bayesianas. Estos modelos gráficos permiten obtener, a primera vista, las principales relaciones entre las variables a considerar. Analizamos el comportamiento de la metodología propuesta con un caso práctico, mostrando que es una herramienta útil para ayudar la toma de decisiones en la elaboración de políticas basadas en indicadores de rendimiento