A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Didactics–promoting collaborative work and personal learning environment. Critical, creative and autonomous roles in a competitive society

School has evolved from a place where knowledge is provided to a place where learners are helped to develop their professional and social skills. Consequently, education must evolve through big challenges in order to face the changes of society in the XXIst century

Supporting graduate development through work based learning

The genesis of this innovation lies in the commitment of a national Irish business enterprise to the professional development of its staff in general, and to the enhancement of its Information Technologies (IT) staff specifically, in collaboration with a national Higher Education (HE) provider. A postgraduate degree, awarded by the HE provider, seeks to bring coherence and cohesion to the education and training provision for newly recruited IT graduate staff of the business enterprise, simultaneously acting both as an induction process for new staff and as a professional capacity building exercise, thereby enhancing the enterprise’s organisational learning and collective competence in the areas of information technologies, IT security and technical service management. The curriculum was designed by the HE provider in collaboration with the business enterprise to offer it to circa sixteen IT staff per cycle of delivery through a model known generally as the new apprenticeship for professional practice which uses a combination of college-based, block release taught elements, regular day release seminars and substantial work-based learning, supported by the academic staff of the HE provider and work-based support staff/mentors of the business enterprise. Academic quality assurance, pedagogical, assessment and accreditation responsibilities remain with the HE provider. (...)

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices

Adaption of Courses and Learning Environment to the User Context in dotLRN

The explosive growth of Internet during the last years has been reflected in the ever-increasing amount of the diversity and heterogeneity of user preferences, types and features of devices and access networks. Usually the heterogeneity in the context of the users which request Web contents is not taken into account by the servers that deliver them implying that these contents will not always suit their needs. In the particular case of e-learning platforms this issue is especially critical due to the fact that it puts at stake the knowledge acquired by their users. In the following paper we present a system that aims to provide the dotLRN e-learning platform with the capability to adapt to its users context. By integrating dotLRN with a multi-agent hypermedia system, online courses being undertaken by students as well as their learning environment are adapted in real time

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Curriculum sequencing for an e-learning system based on learning styles

This work shows the use of adaptation techniques involved in an e-learning system that considers students' learning styles and students' knowledge states. The mentioned e-learning system is built on a multiagent framework designed to examine opportunities to improve the teaching and to motivate the students to learn what they want in a user-friendly and assisted environment

La competència "El treball col·laboratiu": una oportunitat per a incorporar les TIC en la didàctica universitària. Descripció de l'experiència amb la plataforma ACME (UdG) = La competencia "El trabajo colaborativo": una oportunidad para incorporar las TIC en la didáctica universitaria. Descripción de la experiencia con la plataforma ACME (UdG)

L'article és una reflexió sobre els requisits de formació dels professionals que demana la societat del coneixement. Un dels objectius més importants que ha de tenir la universitat en la societat del coneixement és la formació de professionals competents que tinguin prou eines intel·lectuals per a enfrontar-se a la incertesa de la informació, a la consciència que aquesta té una data de caducitat a curt termini i a l'ansietat que això provoca. Però, a més, també han de ser capaços de definir i crear les eines de treball amb què donaran sentit i eficàcia a aquest coneixement mudable i mutant. Per això, l'espai europeu d'ensenyament superior prioritza la competència transversal del treball col·laboratiu amb l'objectiu de promoure un aprenentatge autònom, compromès i adaptat a les noves necessitats de l'empresa del segle xxi. En aquest context, es presenta l'entorn teòric que fonamenta el treball desenvolupat a la plataforma informàtica ACME, que uneix el treball col·laboratiu i l'aprenentatge semipresencial o blended learning. Així mateix, es descriuen amb detall alguns exemples de wikis, paradigma del treball col·laboratiu, fets en assignatures impartides per la Universitat de Girona en l'espai virtual ACME

A teaching/learning support tool for introductory programming courses

In This work we present a Web-based tool developed with the aim of reinforcing teaching and learning of introductory programming courses. This tool provides support for teaching and learning. From the teacher's perspective the system introduces important gains with respect to the classical teaching methodology. It reinforces lecture and laboratory sessions, makes it possible to give personalized attention to the student, assesses the degree of participation of the students and most importantly, performs a continuous assessment of the student's progress. From the student's perspective it provides a learning framework, consisting in a help environment and a correction environment, which facilitates their personal work. With this tool students are more motivated to do programming

Funciones y uso de metáforas en el diseño de entornos e-learning

El objeto de esta comunicación es presentar un estudio exploratorio sobre las funciones y tipologías de metáfora estructurales presentes en productos de e-learning, para estudiar con más detalle dos ejemplos: Geolearning y Activewolds. Estos servirán para realizar un ejercicio de identificación de los elementos audiovisuales presentes en la interface, el tratamiento de las dimensiones espacio y tiempo; el uso de 'avatares' y de las posibilidades de navegación e interacción dentro de los dos sistemas. La teoría de la muldimodalidad (Kress i van Leeuwen) es el marco teórico que conduce la reflexión sobre la representación, orientación y organización de la comunicación. Desde la misma perspectiva teórica, el trabajo de Burn y Parker nos indica pautas metodológicas para el análisis de los productos

Collaborative recommender agents based on case-based reasoning and trust

La comunitat científica que treballa en Intel·ligència Artificial (IA) ha dut a terme una gran quantitat de treball en com la IA pot ajudar a les persones a trobar el que volen dins d'Internet. La idea dels sistemes recomanadors ha estat extensament acceptada pels usuaris. La tasca principal d'un sistema recomanador és localitzar ítems, fonts d'informació i persones relacionades amb els interessos i preferències d'una persona o d'un grup de persones. Això comporta la construcció de models d'usuari i l'habilitat d'anticipar i predir les preferències de l'usuari. Aquesta tesi està focalitzada en l'estudi de tècniques d'IA que millorin el rendiment dels sistemes recomanadors. Inicialment, s'ha dut a terme un anàlisis detallat de l'actual estat de l'art en aquest camp. Aquest treball ha estat organitzat en forma de taxonomia on els sistemes recomanadors existents a Internet es classifiquen en 8 dimensions generals. Aquesta taxonomia ens aporta una base de coneixement indispensable pel disseny de la nostra proposta. El raonament basat en casos (CBR) és un paradigma per aprendre i raonar a partir de la experiència adequat per sistemes recomanadors degut als seus fonaments en el raonament humà. Aquesta tesi planteja una nova proposta de CBR aplicat al camp de la recomanació i un mecanisme d'oblit per perfils basats en casos que controla la rellevància i edat de les experiències passades. Els resultats experimentals demostren que aquesta proposta adapta millor els perfils als usuaris i soluciona el problema de la utilitat que pateixen el sistemes basats en CBR. Els sistemes recomanadors milloren espectacularment la qualitat dels resultats quan informació sobre els altres usuaris és utilitzada quan es recomana a un usuari concret. Aquesta tesi proposa l'agentificació dels sistemes recomanadors per tal de treure profit de propietats interessants dels agents com ara la proactivitat, la encapsulació o l'habilitat social. La col·laboració entre agents es realitza a partir del mètode de filtratge basat en la opinió i del mètode col·laboratiu de filtratge a partir de confiança. Els dos mètodes es basen en un model social de confiança que fa que els agents siguin menys vulnerables als altres quan col·laboren. Els resultats experimentals demostren que els agents recomanadors col·laboratius proposats milloren el rendiment del sistema mentre que preserven la privacitat de les dades personals de l'usuari. Finalment, aquesta tesi també proposa un procediment per avaluar sistemes recomanadors que permet la discussió científica dels resultats. Aquesta proposta simula el comportament dels usuaris al llarg del temps basat en perfils d'usuari reals. Esperem que aquesta metodologia d'avaluació contribueixi al progrés d'aquesta àrea de recerca.