Roboskeleton: an architecture for coordinating Robot Soccer agents

SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.Publicad

A deep learning approach to space weather proxy forecasting for orbital prediction

The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. This has fundamental implications both in the short term, in the day-to-day management of operational spacecraft, and in the mid-to-long term, in determining satellite orbital lifetime. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variabl

Artificial intelligence in support to space traffic management

This paper presents an Artificial Intelligence-based decision support system to assist ground operators to plan and implement collision avoidance manoeuvres. When a new conjunction is expected, the system provides the operator with an optimal manoeuvre and an analysis of the possible outcomes. Machine learning techniques are combined with uncertainty quantification and orbital mechanics calculations to support an optimal and reliable management of space traffic. A dataset of collision avoidance manoeuvres has been created by simulating a range of scenarios in which optimal manoeuvres (in the sense of optimal control) are applied to reduce the collision probability between pairs of objects. The consequences of the execution of a manoeuvre are evaluated to assess its benefits against its cost. Consequences are quantified in terms of the need for additional manoeuvres to avoid subsequent collisions. By using this dataset, we train predictive models that forecast the risk of avoiding new collisions, and use them to recommend alternative manoeuvres that may be globally better for the space environment

Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.This work has been supported by Agencia Estatal de Investigación (PID2019-107455RB-C22 /AEI/ 10.13039/501100011033), and Spanish Ministry of Science and Education under TIN2017-85727-C4-3-P (DeepBio) gran

Adaptation and validation of a scale to assess digital teaching competence in soccer coaches

Since 2015, the Union of European Football Associations (UEFA) has held a convention with the aim of updating its technical qualifications for the training of its football coaches. It has proposed widespread changes, but with a particular recommendation for the creation of a subject about New Technologies. This proposal is in line with the natural evolution of today's society and with an increasing digital presence in all professional fields. The overarching concern for quality teaching in digital competences involves, first and foremost, the necessary adoption of these digital competences by their trainers. As a result, the aim of this research is to adapt and validate a scale, previously validated in other training contexts, to assess the digital teaching competence (hereafter, DTC) of soccer coaches. The study sample consists of 612 coaches at different levels who are registered in the Football Federation of the Valencian Community (hereafter, FFCV). The sample contains a higher percentage of men (91.7%) than women (8.3%) and a mean age of 30.88 years. A confirmatory factor analysis was performed, which revealed five factors grouping 31 indicators: Information and Literacy (5 items), Communication and Collaboration (6 items), Digital Content Creation (9 items), Security (6 items) and Problem Solving (5 items). Goodness-of-Fit indicators showed adequate values: (x2 /gl=2.82); RMSA= 0.6 (Confidence Interval= 0.6-0.7); CFI=.91; IFI= .91. Reliability was tested by Composite Reliability, Cronbach's Alpha and Mean Variance Extracted values. The results of the research support the reliability and validity of the questionnaire that was adapted for the purpose of assessing the digital teaching competence of soccer coaches.Educació

Adaptation and validation of a scale for the evaluation of the professional performance of the football trainer based on his/her continuous training, level of ICT and self-evaluation

La presente investigación tiene por objetivo adaptar y validar una escala para la evaluación del desempeño profesional de los entrenadores de fútbol basada en su formación previa y en el nivel de competencias digitales que posea dicho entrenador (Escala para la evaluación del desempeño profesional del entrenador de fútbol en base a su formación permanente, nivel de TIC y autoevaluación). La muestra del estudio está constituida por un total de 412 entrenadores en formación, de los cuales el 91.7% son hombres y el 8,3% restante son mujeres. Todos estos sujetos son alumnos de los cursos de formación de entrenadores de fútbol de la Federación de Fútbol de la Comunidad Valenciana (FFCV), distribuidos por los diferentes niveles técnicos, aunque el más representado es el UEFA C con un 71.1%. El diseño de la escala se realizó a partir del cuestionario realizado por Zambrano, Meda y Lara (2005). Se realizó un análisis factorial exploratorio (AFE) y un análisis factorial confirmatorio (AFC), que permitieron identificar tres factores en los que se agruparon los indicadores: interés por la formación continua (4 ítems), formación en TIC (3 ítems) y autoevaluación (4 ítems). Los diferentes indicadores de bondad de ajuste mostraron valores adecuados: (x2 /gl=2.77); RMSEA=.057 (Intervalo de confianza=.042-.072); CFI=.97; IFI=.97. Se comprobó la fiabilidad mediante los valores de fiabilidad compuesta, alfa de Cronbach y la varianza media extraída. Los resultados de la investigación sostienen la fiabilidad y validez de la escala para valorar el desempeño profesional de los entrenadores de fútbol.This research aims to adapt and validate a scale for the evaluation of the professional performance of football coaches based on their previous training and the level of digital competencies that the coach possesses (Scale for the evaluation of the professional performance of football coaches based on their permanent training, level of ICT and self-evaluation).The study sample is made up of a total of 412 coaches in training, of which 91.7% are men and the remaining 8.3% are women.All of these subjects are students of the training courses for football coaches of the Valencian Football Federation (FFCV), distributed by the different technical levels, although the most represented is UEFA C with 71.1%.The design of the scale was based on the questionnaire carried out by Zambrano, Meda and Lara (2005). An exploratory factorial analysis (EFA) and a confirmatory factorial analysis (CFA) were carried out, which allowed identifying three factors in which the indicators were grouped: interest in continuous training (4 items), ICT training (3 items) and self-evaluation (4 items).The different goodness- of-fit indicators showed adequate values: (x2 /gl=2.77); RMSEA=.057 (Confidence Interval=.042-.072); CFI=.97; IFI=.97. Reliability was checked using the composite reliability values, Cronbach’s alpha and the average variance extracted.The results of the research support the reliability and validity of the scale to assess the professional performance of soccer coaches.Ciencias de la Actividad Física y del Deport

Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation

Subtitles are a key element to make any media content accessible for people who suffer from hearing impairment and for elderly people, but also useful when watching TV in a noisy environment or learning new languages. Most of the time, subtitles are generated manually in advance, building a verbatim and synchronised transcription of the audio. However, in TV live broadcasts, captions are created in real time by a re-speaker with the help of a voice recognition software, which inevitability leads to delays and lack of synchronisation. In this paper, we present Deep-Sync, a tool for the alignment of subtitles with the audio-visual content. The architecture integrates a deep language representation model and a real-time voice recognition software to build a semantic-aware alignment tool that successfully aligns most of the subtitles even when there is no direct correspondence between the re-speaker and the audio content. In order to avoid any kind of censorship, Deep-Sync can be deployed directly on users' TVs causing a small delay to perform the alignment, but avoiding to delay the signal at the broadcaster station. Deep-Sync was compared with other subtitles alignment tool, showing that our proposal is able to improve the synchronisation in all tested cases.This work has been supported by the Spanish Ministry of Science and Education under TIN2017-85727-C4-3-P grant (DeepBio) and Comunidad Autónoma de Madrid under S2018/TCS-4566 grant (CYNAMON). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research

An intelligent system for robust decision-making in the all-vs-all conjunction screening problem

The progressive increase of traffic in space demands new approaches for supporting automatic and robust operational decisions. CASSANDRA, Computational Agent for Space Situational Awareness aNd Debris Remediation Automation, is an intelligent system for Space Environment Management (SEM) intended to assist operators with the management of space traffic by providing robust decision-making support. This paper will present the automatic conjunction screening and collision avoidance manoeuvre pipeline within CASSANDRA, connecting the some of CASSANDRA’s modules: Automated Conjunction Screening (ACS), Robust State Estimation (RSE), Intelligent Decision Support System (IDSS) and Collision Avoidance Manoeuvres (CAM). The pipelines allows to screen the catalogue to detect potential conjunctions, perform a detailed analysis of the encounter accounting for uncertainty (aleatory and epistemic) and new observations, provide robust decisions based on the available information and, if necessary, proposed robust optimal CAMs and analyse the impact of the new orbit on the background population. This paper will present the pipeline described above along with an example that illustrates how CASSANDRA can be used to generate robust decisions on the execution of CAMs in an automated way