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

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

Aprendizaje autorregulado, creencias de autoeficacia y desempeño en la segunda infancia

Este artículo analiza relaciones entre el aprendizaje autorregulado, las creencias de autoeficacia y el desempeño en tareas de resolución de problemas aritméticos. El estudio se ha llevado a cabo con 268 escolares de seis años de edad y matriculados en el primer año de educación primaria en España. Los resultados obtenidos mediante modelos de regresión logística binaria indican que el aprendizaje autorregulado y su interacción con las creencias de autoeficacia predicen el desempeño. Por último, la aplicación de un análisis Cluster muestra cuatro perfiles de escolares, denominados: i) ajustado positivo; ii) desajustado negativo I; iii) desajustado negativo II y; iv) ajustado negativo

Cost-Effective PEDOT:PSS Temperature Sensors Inkjetted on a Bendable Substrate by a Consumer Printer

In this work, we report on a fabrication protocol to produce fully inkjet-printed temperature sensors on a bendable polyethylene terephthalate (PET) substrate. The sensing layer is made of polymer-based Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) ink that is electrically contacted by an underlying interdigitated electrode (IDE) structure based on a silver nanoparticle (AgNP) ink. Both inks are available commercially, and no further ink processing is needed to print them using a cost-effective consumer printer with standard cartridges. The fabricated sensor modules are tested for different IDE dimensions and post-deposition treatments of the AgNP film for their response to a temperature range of 20 to 70 °C and moisture range of 20 to 90% RH (relative humidity). Attributed to the higher initial resistance, sensor modules with a larger electrode spacing of 200 µm show a higher thermal sensitivity that is increased by a factor of 1.8 to 2.2 when compared to sensor modules with a 150 µm-spacing. In all cases, the sensors exhibit high linearity towards temperature and a response comparable to state of the art.This research was funded by the European Union through the fellowship H2020-MSCA-IF-2017 794885-SELFSENS and the TUM Graduate Schoo

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

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

Nuevas perspectivas en la orientación educativa al alumnado extranjero

Se pretende reflexionar en torno a la sociedad emergida caracterizada principalmente por el fenómeno migratorio así como por la llamada era de la comunicación e información y la respuesta educativa capaz de afrontar eficazmente las situaciones que generan; respuesta que debe ser liderada desde el campo de la orientación educativa. Analizamos los principios que deberán fundamentar actualmente la acción orientadora, fundamentalmente el de justicia social y el de comunicación e intercambio. En el aula, la orientación estará enmarcada por un modelo triádico, actuando en colaboración con el resto de agentes para lo que proponemos contextos propiciados por nuevos formatos de trabajo en el aula, especialmente por el desarrollo de aprendizaje cooperativo._____________________________We have tried to reflect around the emerged society that comes mainly characterized by the migratory phenomenon us well us be the call it was of the communication and information and the educative answer able to effectively confront the situations that they generate; answer that we are convinced ,must be led from the field of the educative direction. We contributed new principles that would have to protect the orientation action, the one of social justice and the one uf communication and interchange. We finalized proposing that one of the routes nails will come from the hand of new formats of work in the classroom, specially by the development of which it is denominated like cooperative learning

Parameter learning in MTE networks using incomplete data

Bayesian networks with mixtures of truncated exponentials (MTEs) are gaining popularity as a flexible modelling framework for hybrid domains. MTEs support efficient and exact inference algorithms, but estimating an MTE from data has turned out to be a difficult task. Current methods suffer from a considerable computational burden as well as the inability to handle missing values in the training data. In this paper we describe an EM- based algorithm for learning the maximum likelihood parameters of an MTE network when confronted with incomplete data. In order to overcome the computational difficulties we make certain distributional assumptions about the domain being modeled, thus focusing on a subclass of the general class of MTE networks. Preliminary empirical results indicate that the proposed method offers results that are inline with intuition

The relationship of gender, time orientation, and achieving self-regulated learning

The present study has three objectives: i) to test a theoretical model of academic achievement modulated by self-regulated learning; ii) to analyze significant differences between self-regulated learning means and time patterns depending on the gender of the participants; and iii) to identify self-regulated learning profiles toward academic achievement based on gender. The data were obtained from a sample of 192 university students in education degrees who were administered three instruments: a Future Time Orientation subscale by Zimbardo and Boyd, (1999); the 2x2 Achievement Goals Scale by Elliot and McGregor (2001), and a Learning Regulation subscale by Vermunt (1998). In addition, they answered a question about their mean grade point average up to that point in the academic degree studied. The results indicate a significant and positive relationship between the future time orientation, approach goals, and external regulation strategies. Significantly higher scores are observed in women than in men on key academic performance variables, although the effect size was not large. The gender differences are exclusively quantitative. In both groups, the characteristics of the learner profiles are somewhat similar, with no important differences observed for the gender condition. In general, avoidance goals and external regulation strategies can lead to success in academic achievement, as long as they are accompanied by a future orientation, internal regulation, and approach goals.The present study has three objectives: i) to test a theoretical model of academic achievement modulated by self-regulated learning; ii) to analyze significant differences between self-regulated learning means and time patterns depending on the gender of the participants; and iii) to identify self-regulated learning profiles toward academic achievement based on gender. The data were obtained from a sample of 192 university students in education degrees who were administered three instruments: a Future Time Orientation subscale by Zimbardo and Boyd, (1999); the 2x2 Achievement Goals Scale by Elliot and McGregor (2001), and a Learning Regulation subscale by Vermunt (1998). In addition, they answered a question about their mean grade point average up to that point in the academic degree studied. The results indicate a significant and positive relationship between the future time orientation, approach goals, and external regulation strategies. Significantly higher scores are observed in women than in men on key academic performance variables, although the effect size was not large. The gender differences are exclusively quantitative. In both groups, the characteristics of the learner profiles are somewhat similar, with no important differences observed for the gender condition. In general, avoidance goals and external regulation strategies can lead to success in academic achievement, as long as they are accompanied by a future orientation, internal regulation, and approach goals