"In territorio de Carrion in Ualle de Quoza": representación del espacio, identidad y conflicto político en el territorio de Carrión (siglos X-XII)

This essay considers the way in which the territory of Carrión, in the Duero basin, was represented in the charters between the 10th and the 12th centuries. This space remained under the political control of the Banu Gómez since the late 10th century. During the second half of the 11th century, when the control of this aristocratic group was particularly intense, the territory, which had so far been always called Carrión, appears in some charters as Santa Maria. Some decades later, as royal power was reasserted over the territory, the place-name Carrión became again the norm. This essay seeks to analyse the specific documentary contexts in which these place-names appear in order to see how they relate to other factors in the production of the charters and what might have caused those changes. Ultimately, it seeks to evaluate how the analysis of place-name attribution and changes over time might help us get a better understanding of the relations and structures of power in the Duero basin during this period.Este trabajo plantea un análisis de las formas en que se representó en la documentación de los siglos x y XII el territorio de Carrión, en la meseta del Duero. Este espacio estuvo bajo el dominio del grupo aristocrático de los Banu Gómez desde finales del siglo X. Durante la segunda mitad del siglo XI, en un periodo en el que el control de este grupo aristocrático parece volverse particularmente intenso, el territorio comienza a aparecer en algunos documentos como Santa María. Unas décadas después, coincidiendo con el afianzamiento del poder regio sobre el territorio, se vuelve de nuevo a utilizar, prácticamente en exclusiva, el término Carrión. Este estudio plantea un análisis de los contextos concretos en los que se produjeron los documentos en los que se consignan cada uno de esos topónimos con el fin de analizar a qué otros elementos de la producción de los documentos se asocia el uso de uno u otro topónimo y cuáles pudieron ser las causas de esos cambios. En último término, plantea la importancia del análisis de los procesos de fijación y transformación de los topónimos como una vía para el estudio de las relaciones y estructuras de poder en la meseta del Duero durante esta época

Bio-inspired decision making system for an autonomous social robot: the role of fear

Robotics is an emergent field which is currently in vogue. In the near future, many researchers anticipate the spread of robots coexisting with humans in the real world. This requires a considerable level of autonomy in robots. Moreover, in order to provide a proper interaction between robots and humans without technical knowledge, these robots must behave according to the social and cultural norms. This results in social robots with cognitive capabilities inspired by biological organisms such as humans or animals. The work presented in this dissertation tries to extend the autonomy of a social robot by implementing a biologically inspired decision making system which allows the robot to make its own decisions. Considering this kind of decision making system, the robot will not be considered as a slave any more, but as a partner. The decisionmaking systemis based on drives,motivations, emotions, and self-learning. According to psychological theories, drives are deficits of internal variables or needs (e.g. energy) and the urge to correct these deficits are the motivations (e.g. survival). Following a homeostatic approach, the goal of the robot is to satisfy its drives maintaining its necessities within an acceptable range, i.e. to keep the robot’s wellbeing as high as possible. The learning process provides the robot with the proper behaviors to cope with each motivation in order to achieve the goal. In this dissertation, emotions are individually treated following a functional approach. This means that, considering some of the different functions of emotions in animals or humans, each artificial emotion plays a different role. Happiness and sadness are employed during learning as the reward or punishment respectively, so they evaluate the performance of the robot. On the other hand, fear plays a motivational role, that is, it is considered as a motivation which impels the robot to avoid dangerous situations. The benefits of these emotions in a real robot are detailed and empirically tested. The robot decides its future actions based on what it has learned from previous experiences. Although the current context of this robot is limited to a laboratory, the social robot cohabits with humans in a potentially non-deterministic environment. The robot is endowed with a repertory of actions but, initially, it does not know what action to execute either when to do it. Actually, it has to learn the policy of behavior, i.e. what action to execute in different world configuration, that is, in every state, in order to satisfy the drive related to the highest motivation. Since the robot will be learning in a real environment interacting with several objects, it is desired to achieve the policy of behavior in an acceptable range of time. The learning process is performed using a variation of the well-known Q-Learning algorithm, the Object Q-Learning. By using this algorithm, the robot learns the value of every state-action pair through its interaction with the environment. This means, it learns the value that every action has in every possible state; the higher the value, the better the action is in that state. At the beginning of the learning process these values, called the Q values, can all be set to the same value, or some of them can be fixed to another value. In the first case, this implies that the robot will learn from scratch; in the second case, the robot has some kind of previous information about the action selection. These values are updated during the learning process. The emotion of fear is particularly studied. The generation process of this emotion (the appraisal) and the reactions to fear are really useful to endow the robot with an adaptive reliable mechanism of “survival”. This dissertation presents a social robot which benefits from a particular learning process of new releasers of fear, i.e. the capacity to identify new dangerous situations. In addition, by means of the decision making system, the robot learns different reactions to prevent danger according to different unpredictable events. In fact, these reactions to fear are quite similar to the fear reactions observed in nature. Another challenge is to design a solution for the decision making system in such a way that it is flexible enough to easily change the configuration or even apply it to different robots. Considering the bio-inspiration of this work, this research (and other related works) was born as a try to better understand the brain processes. It is the author’s hope that it sheds some light in the study of mental processes, in particular those which may lead to mental or cognitive disorders. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La robótica es un área emergente que actualmente se encuentra en boga. Muchos científicos pronostican que, en un futuro próximo, los robot cohabitarán con las personas en el mundo real. Para que esto llegue a suceder, se necesita que los robots tengan un nivel de autonomía considerable. Además, para que exista una interacción entre robots y personas sin conocimientos técnicos, estos robots deben comportarse de acuerdo a las normas sociales y culturales. Esto nos lleva a robots sociales con capacidades cognitivas inspiradas en organismos biológicos, como los humanos o los animales. El trabajo que se presenta en esta tesis pretende aumentar la autonomía de un robot social mediante la implementación de un sistema de toma de decisiones bioinspirado que permita a un robot tomar sus propias decisiones. Desde este punto de vista, el robot no se considerará más como un esclavo, sino como un compañero. El sistema de toma de decisiones está basado en necesidades (drives), motivaciones, emociones y auto-aprendizaje. De acuerdo a diversas teorías psicológicas, las necesidades son carencias o déficits de variables internas (por ejemplo, la energía) y el impulso para corregir estas necesidades son las motivaciones (como por ejemplo la supervivencia). Considerando un enfoque homeostático, el objetivo del robot es satisfacer sus carencias manteniéndolas en un nivel aceptable. Esto quiere decir que el bienestar del robot debe ser lo más alto posible. El proceso de aprendizaje permite al robot desarrollar el comportamiento necesario según las distintas motivaciones para lograr su objetivo. En esta tesis, las emociones son consideradas de forma individual desde un punto de vista funcional. Esto significa que, considerando las diferentes funciones de las emociones en animales y humanos, cada una de las emociones artificiales juega un papel diferente. Por un lado, la felicidad y la tristeza se usan durante el aprendizaje como refuerzo o castigo respectivamente y, por tanto, evaluan el comportamiento del robot. Por otro lado, el miedo juega un papel motivacional, es decir, es considerado como una motivación la cual “empuja” el robot a evitar las situaciones peligrosas. Los detalles y las ventajas de estas emociones en un robot real se muestran empíricamente a lo largo de este libro. El robot decide sus acciones futuras en base a lo que ha aprendido en experiencias pasadas. A pesar de que el contexto actual del robot está limitado a un laboratorio, el robot social cohabita con personas en un entorno potencialmente no-determinístico. El robot está equipado con un repertorio de acciones pero, inicialmente, no sabe qué acción ejecutar ni cuando hacerlo. De echo, tiene que aprender la política de comportamiento, esto es, qué acción ejecutar en diferentes configuraciones del mundo (en cada estado) para satisfacer la necesidad relacionada con la motivación más alta. Puesto que el robot aprende en un entorno real interaccionando con distintos objetos, es necesario que este aprendizaje se realice en un tiempo aceptable. El algoritmo de aprendizaje que se utiliza es una variación del conocido Q-Learning, el Object Q-Learning. Mediante este algoritmo el robot aprende el valor de cada par estadoacción a través de interacción con el entorno. Esto significa, que aprende el valor de cada acción in cada posible estado. Cuanto más alto sea el valor, mejor es la acción en ese estado. Al inicio del proceso de aprendizaje, estos valores, llamados valores Q, pueden tener todos el mismo valor o pueden pueden tener asignados distintos valores. En el primer caso, el robot no dispone de conocimientos previos; en el segundo, el robot dispone de cierta información sobre la acción a elegir. Estos valores serán actualizados durante el aprendizaje. La emoción de miedo es especialmente estudiada en esta tesis. La forma de generarse esta emoción (el appraisal) y las reacciones al miedo resultan realmente útiles a la hora de dotar al robot con un mecanismo de supervivencia adaptable y fiable. Esta tesis presenta un robot social que utiliza un proceso particular para el aprendizaje de nuevos “liberadores” del miedo, es decir, dispone de la capacidad de identificar nuevas situaciones peligrosas. Además, mediante el sistema de toma de decisiones, el robot aprende diferente reacciones para protegerse ante posibles daños causados por diversos eventos impredecibles. De echo, estas reacciones al miedo son bastante similares a las reacciones al miedo que se pueden observar en la naturaleza. Otro reto importante es el diseño de la solución: el sistema de toma de decisiones tiene que diseñarse de forma que sea suficientemente flexible para permitir cambiar fácilmente la configuración o incluso para aplicarse a distintos robots. Teniendo en cuenta el enfoque bioinspirado de este trabajo, esta investigación (y muchos otros trabajos relacionados) surge como un intento de entender un poco más lo que sucede en el cerebro. El autor espera que esta tesis pueda ayudar en el estudio de los procesos mentales, en particular aquellos que pueden llevar a desórdenes mentales o cognitivos

Shanghai Lectures

Jornada de Innovación Docente: resultados y estrategias, celebrada el 22 de junio de 2016 en la Universidad Carlos III de Madrid, donde se presentan algunos de los proyectos de innovación docente del curso 2015-2016

Effects of form and motion on judgments of social robots' animacy, likability, trustworthiness and unpleasantness

One of robot designers' main goals is to make robots as sociable as possible. Aside from improving robots' actual social functions, a great deal of effort is devoted to making them appear lifelike. This is often achieved by endowing the robot with an anthropomorphic body. However, psychological research on the perception of animacy suggests another crucial factor that might also contribute to attributions of animacy: movement characteristics. In the current study, we investigated how the combination of bodily appearance and movement characteristics of a robot can alter people's attributions of animacy, likability, trustworthiness, and unpleasantness. Participants played games of Tic-Tac-Toe against a robot which (1) either possessed a human form or did not, and (2) either exhibited smooth, lifelike movement or did not. Naturalistic motion was judged to be more animate than mechanical motion, but only when the robot resembled a human form. Naturalistic motion improved likeability regardless of the robot's appearance. Finally, a robot with a human form was rated as more disturbing when it moved naturalistically. Robot designers should be aware that movement characteristics play an important role in promoting robots' apparent animacy.This work was partially supported by the Spanish Government through the project call "Aplicaciones de los robots sociales", DPI2011-26980 from the Spanish Ministry of Economy and Competitiveness. Álvaro Castro-González was partially supported by a grant from Universidad Carlos III de Madrid

Osteología: relevancia de conceptos médicos en el ámbito odontológico

ResumenCaries y enfermedad periodontal son enfermedades odontológicas de altísima prevalencia en todo el mundo. El mismo patrón se observa con la osteoporosis, enfermedad ósea que, debido a la inversión de la pirámide demográfica, en gran parte de la población mundial no dejará de ir en aumento.Tejido óseo y dientes presentan una íntima relación anatómica y funcional. Por esto creemos útil dilucidar, entre otros tantos aspectos, si la «osteoporosis» afecta a los huesos maxilares y a la mandíbula, del modo como sí lo hace en huesos largos, en especial en aquellos de mujeres posmenopáusicas.De ser así, la terapia farmacológica de amplia utilización mundial, bifosfonatos, podría también favorecer los huesos maxilares y mandíbula y, entonces, los odontólogos no solo nos enfocaríamos en el reporte epidemiológico y etiopatogénico de su potencial efecto adverso: «osteonecrosis mandibular asociada al uso de bifosfonatos» (OMRB).Consecuentemente, Osteología y Odontología tienen más de un punto en común, tanto en el ámbito fisiológico como en el fisiopatológico.AbstractCavities and periodontal diseases have a very high prevalence worldwide. Similarly, osteoporosis, another disease affecting bones, will continue to increasing because of the so called «reversal of the demographic pyramid».Bone and teeth are both anatomical and functional related. Therefore, it is worth determining, among many other aspects, whether osteoporosis also affects the maxilla and mandibular bones, in the same way as in the long bones, especially those of post-menopausal women.If so, the pharmacological therapy most widely used in the world, bisphosphonates, could also work in maxilla and mandibular bones and then would allow dentists to focus not only on reporting epidemiological and etiopathogenic data on its potential adverse effect: «bisphosphonate-related osteonecrosis of the jaw» (BRONJ).Consequently, Osteology and Odontology could share more than one common issue, as well in the physiological as in the pathophysiological field

An autonomous social robot in fear

Currently artificial emotions are being extensively used in robots. Most of these implementations are employed to display affective states. Nevertheless, their use to drive the robot's behavior is not so common. This is the approach followed by the authors in this work. In this research, emotions are not treated in general but individually. Several emotions have been implemented in a real robot, but in this paper, authors focus on the use of the emotion of fear as an adaptive mechanism to avoid dangerous situations. In fact, fear is used as a motivation which guides the behavior during specific circumstances. Appraisal of fear is one of the cornerstones of this work. A novel mechanism learns to identify the harmful circumstances which cause damage to the robot. Hence, these circumstances elicit the fear emotion and are known as fear releasers. In order to prove the advantages of considering fear in our decision making system, the robot's performance with and without fear are compared and the behaviors are analyzed. The robot's behaviors exhibited in relation to fear are natural, i.e., the same kind of behaviors can be observed on animals. Moreover, they have not been preprogrammed, but learned by real interactions in the real world. All these ideas have been implemented in a real robot living in a laboratory and interacting with several items and people.The funds have been provided by the Spanish Government through the project called "A new approach to social robotics" (AROS), of MICINN (Ministry of Science and Innovation) and through the RoboCity2030- II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Learning the selection of actions for an autonomous social robot by reinforcement learning based on motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot's needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment. The used reinforcement learning method exploits a new algorithm called Object Q-Learning. The proposed reduction of the state space and the new algorithm considering the collateral effects (relationship between different objects) results in a suitable algorithm to be applied to robots living in real environments. In this paper, a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot's performance. The quality of its performance will be determined by observing the evolution of the robot's wellbeing.The funds provided by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), and the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

A Convolutional Approach to Quality Monitoring for Laser Manufacturing

[Abstract] The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing

Maggie: A Social Robot as a Gaming Platform

Edutainment robots are robots designed to participate in people's education and in their entertainment. One of the tasks of edutainment robots is to play with their human partners, but most of them offer a limited pool of games. Moreover, it is difficult to add new games to them. This lack of flexibility could shorten their life cycle. This paper presents a social robot on which several robotic games have been developed. Our robot uses a flexible and modular architecture that allows the creation of new skills by the composition of existing and simpler skills. With this architecture, the development of a new game mainly consists in the composition of the skills that are needed for this specific game. In this paper, we present the robot, its hardware and its software architecture, including its interaction capabilities. We also provide a detailed description of the development of five of the games the robot can play.The authors gratefully acknowledge the funds provided by the Spanish MICINN (Ministry of Science and Innovation) through the project "A new Approach to Social Robotics (AROS)". The research leading to these results has also received funding from the RoboCity2030-II-CM project(S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds ofthe EU

Speeding-up Action Learning in a Social Robot with Dyna-Q+: A Bioinspired Probabilistic Model Approach

Robotic systems that are developed for social and dynamic environments require adaptive mechanisms to successfully operate. Consequently, learning from rewards has provided meaningful results in applications involving human-robot interaction. In those cases where the robot's state space and the number of actions is extensive, dimensionality becomes intractable and this drastically slows down the learning process. This effect is specially notorious in one-step temporal difference methods because just one update is performed per robot-environment interaction. In this paper, we prove how the action-based learning of a social robot can be improved by combining classical temporal difference reinforcement learning methods, such as Q-learning or Q( λ), with a probabilistic model of the environment. This architecture, which we have called Dyna, allows the robot to simultaneously act and plan using the experience obtained during real human-robot interactions. Principally, Dyna improves classical algorithms in terms of convergence speed and stability, which strengthens the learning process. Hence, in this work we have embedded a Dyna architecture in our social robot, Mini, to endow it with the ability to autonomously maintain an optimal internal state while living in a dynamic environment