Combining Optimization and Machine Learning for the Formation of Collectives

This thesis considers the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility and cooperative learning). Such problems require fast approaches that can produce solutions of high quality for hundreds of agents. With this goal in mind, existing solutions for the formation of collectives focus on enhancing the optimization approach by exploiting the characteristics of a domain. However, the resulting approaches rely on specific domain knowledge and are not transferable to other collective formation problems. Therefore, approaches that can be applied to various problems need to be studied in order to obtain general approaches that do not require prior knowledge of the domain. Along these lines, this thesis proposes a general approach for the formation of collectives based on a novel combination of machine learning and an \emph{Integer Linear Program}. More precisely, a machine learning component is trained to generate a set of promising collectives that are likely to be part of a solution. Then, such collectives and their corresponding utility values are introduced into an \emph{Integer Linear Program} which finds a solution to the collective formation problem. In that way, the machine learning component learns the structure shared by ``good'' collectives in a particular domain, making the whole approach valid for various applications. In addition, the empirical analysis conducted on two real-world domains (i.e., ridesharing and team formation) shows that the proposed approach provides solutions of comparable quality to state-of-the-art approaches specific to each domain. Finally, this thesis also shows that the proposed approach can be extended to problems that combine the formation of collectives with other optimization objectives. Thus, this thesis proposes an extension of the collective formation approach for assigning pickup and delivery locations to robots in a warehouse environment. The experimental evaluation shows that, although it is possible to use the collective formation approach for that purpose, several improvements are required to compete with state-of-the-art approaches. Overall, this thesis aims to demonstrate that machine learning can be successfully intertwined with classical optimization approaches for the formation of collectives by learning the structure of a domain, reducing the need for ad-hoc algorithms devised for a specific application

Improving HPC applications scheduling with predictions based on automatically collected historical data

This work analyses the benefits of a system in an HPC environment, which being able to get real performance data from jobs, uses it for future scheduling in order to improve the performance of the applications with minimal user input

La sensorialidad como estrategia para la educación patrimonial en el aula de educación infantil

Resumen: Con este artículo se pretende poner en valor la actividad sensorial como estrategia para la educación patrimonial en el aula de infantil. Tras una primera parte de revisión teórica, el artículo pretende demostrar, a través de un diseño educativo realizado al efecto e implementado en el aula de infantil, la relación entre la sensorialidad y los procesos de patrimonialización en edades tempranas. Este diseño nos permite establecer una secuencia didáctica que parte del conocimiento sensorial, como base del aprendizaje significativo en la etapa de infantil y que permite establecer canales para la vinculación afectiva entre sujeto y objeto. Palabras clave: Educación patrimonial, actividad sensorial, procesos de patrimonialización, educación infantil.    Abstract: This article aims to give value to the sensory activity as a strategy for heritage education in the classroom child. After a first part of theoretical review, the article seeks to demonstrate, through an educational purpose design made and implemented in the classroom child, the relationship between the sensory and heritagization processes at an early age. This design allows us to establish a teaching sequence that part of sensory knowledge, as a basis for meaning full earning in the infant stage and allows to establish channels for the emotional connection between subject and object.Keywords : heritage education, sensory activity, heritagization processes, early childhood education.http://dx.doi.org/10.7203/eari.7.8010  

Rendimiento deportivo, estilos de liderazgo y evitación experiencial en jóvenes futbolistas almerienses

Suplemento de artículos seleccionados con revisión del Congreso Andaluz de Psicología de la Actividad Física y el Deporte (13 : 2011 : Sevilla). Editores : José Carlos Caracuel ; Rocío Bohórquez.Multiple studies have shown the effectiveness of Acceptance and Commitment Therapy (ACT) in enhancing sport performance (SP) -canoeing, tennis, handball, chess, etc.- since experimental avoidance (EA) plays an important role therein. This study addresses the relationship between EA and the psychological variables linked to SP in young footballers (Club Deportivo Vera) and between EA and leadership coaching. This study is justified by the possibility of improving SP by ACT protocols aimed at the acceptance of private events and bodily states and focuses on their behaviour in implementing their values. The instruments used were the Acceptance and Action Questionnaire II (AAQ-II) -which measures the degree of EA- the Psychological Characteristics Related to Sport Performance Questionnaire (CPRD) and the Sport Leadership Scale (LSS). The results show that the relationships (-.457) were statistically significant (p < .01) for EA and the psychological characteristics associated with SP. Likewise, the players' preference for autocratic leadership behaviour negatively correlated to these variables. On the basis of these result, we propose implementing ACT protocols to enhance players' SP