Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Benchmarking sobre técnicas de otimização para modelos de apoio à decisão na medicina intensiva

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

The Value of Imperfection : the Wabi-Sabi Principle in Aesthetics and Learning

OER-based learning has the potential to overcome many shortcomings and problems of traditional education. It is not hampered by IP restrictions; can depend on collaborative, cumulative, iterative refinement of resources; and the digital form provides unprecedented flexibility with respect to configuration and delivery. The OER community is a progressive group of educators and learners with decades of learning research to draw from, who know that we must prepare learners for an evolving and diverse reality. Despite this OER tends to replicate the unsuccessful characteristics of traditional education. To remedy this we may need to remember the importance of imperfection, mistakes, problems, disagreement, and the incomplete for engaged learning, and relinquish our notions of perfection, acknowledging that learners learn differently and we need diverse learners. We must stretch our perceptions of quality and provide mechanisms for engaging the incredible pool of educators globally to fulfill the promise of inclusive education.

Reusing IMS-LD formalized best practices in collaborative learning structuring

Designs of CSCL (Computer Supported Collaborative Learning)activities should be flexible, effective and customizable toparticular learning situations. On the other hand, structureddesigns aim to create favourable conditions for learning. Thus,this paper proposes the collection of representative and broadlyaccepted (best practices) structuring techniques in collaborative learning. With the aim of establishing a conceptual common ground among collaborative learning practitioners and softwaredevelopers, and reusing the expertise that best practicesrepresent, the paper also proposes the formulation of these techniques as patterns: the so-called CLFPs (CollaborativeLearning Flow Patterns). To formalize these patterns, we havechosen the educational modelling language IMS Learning Design (IMS-LD). IMS-LD has the capability to specify many of the collaborative characteristics of the CLFPs. Nevertheless, the language bears limited capability for describing the services that mediate interactions within a learning activity and the specification of temporal or rotated roles. This analysis isdiscussed in the paper, as well as our approaches towards thedevelopment of a system capable of integrating tools using IMSLDscripts and a CLFP-based Learning Design authoring tool.

Diseño, implementación y evaluación de un juego educativo basado en puzles con scaffolding orientado a la programación

Existe una falta de interés por parte de los estudiantes en el área de las Tecnologías de la Información y la Comunicación (TIC) que se ve reflejada en el descenso de las matriculaciones en este ámbito. El uso de metodologías de aprendizaje basadas en el Constructivismo combinadas con tecnología software, se ha observado que es una buena solución para afrontar dicha falta de interés. Sin embargo, actualmente no existen aplicaciones software que implementen estas metodologías pedagógicas y que proporcionen a los estudiantes los mecanismos de ayuda necesarios (Scaffolding) para darles soporte durante el aprendizaje de conceptos TIC. Una posible solución a este problema es el uso de juegos educativos, los cuáles implementarán técnicas de Scaffolding que den el soporte necesario al estudiante para alcanzarlos objetivos de aprendizaje fijados. Por tanto, en este proyecto se diseñará e implementará un juego educativo basado en puzles orientado a la Programación que estará basado en un método aprendizaje basado en el Constructivismo en el que el estudiante construye su propio conocimiento. Una vez implementado, será evaluado en un centro escolar por parte deestudiantes de últimos cursos de ESO o Bachillerato.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Apprentissage automatique de la morphologie : le cas des structures racine-schème

RÉSUMÉ Cette thèse porte sur le développement de méthodes algorithmiques pour découvrir automatiquement la structure morphologique des mots d'un corpus. On considère en particulier le cas des langues s'approchant du type introflexionnel, comme l'arabe ou l'hébreu. La tradition linguistique décrit la morphologie de ces langues en termes d'unités discontinues : les racines consonantiques et les schèmes vocaliques. Ce genre de structure constitue un défi pour les systèmes actuels d'apprentissage automatique, qui opèrent généralement avec des unités continues. La stratégie adoptée ici consiste à traiter le problème comme une séquence de deux sous-problèmes. Le premier est d'ordre phonologique : il s'agit de diviser les symboles (phonèmes, lettres) du corpus en deux groupes correspondant autant que possible aux consonnes et voyelles phonétiques. Le second est de nature morphologique et repose sur les résultats du premier : il s'agit d'établir l'inventaire des racines et schèmes du corpus et de déterminer leurs règles de combinaison. On examine la portée et les limites d'une approche basée sur deux hypothèses : (i) la distinction entre consonnes et voyelles peut être inférée sur la base de leur tendance à alterner dans la chaîne parlée; (ii) les racines et les schèmes peuvent être identifiés respectivement aux séquences de consonnes et voyelles découvertes précédemment. L'algorithme proposé utilise une méthode purement distributionnelle pour partitionner les symboles du corpus. Puis il applique des principes analogiques pour identifier un ensemble de candidats sérieux au titre de racine ou de schème, et pour élargir progressivement cet ensemble. Cette extension est soumise à une procédure d'évaluation basée sur le principe de la longueur de description minimale, dans- l'esprit de LINGUISTICA (Goldsmith, 2001). L'algorithme est implémenté sous la forme d'un programme informatique nommé ARABICA, et évalué sur un corpus de noms arabes, du point de vue de sa capacité à décrire le système du pluriel. Cette étude montre que des structures linguistiques complexes peuvent être découvertes en ne faisant qu'un minimum d'hypothèses a priori sur les phénomènes considérés. Elle illustre la synergie possible entre des mécanismes d'apprentissage portant sur des niveaux de description linguistique distincts, et cherche à déterminer quand et pourquoi cette coopération échoue. Elle conclut que la tension entre l'universalité de la distinction consonnes-voyelles et la spécificité de la structuration racine-schème est cruciale pour expliquer les forces et les faiblesses d'une telle approche. ABSTRACT This dissertation is concerned with the development of algorithmic methods for the unsupervised learning of natural language morphology, using a symbolically transcribed wordlist. It focuses on the case of languages approaching the introflectional type, such as Arabic or Hebrew. The morphology of such languages is traditionally described in terms of discontinuous units: consonantal roots and vocalic patterns. Inferring this kind of structure is a challenging task for current unsupervised learning systems, which generally operate with continuous units. In this study, the problem of learning root-and-pattern morphology is divided into a phonological and a morphological subproblem. The phonological component of the analysis seeks to partition the symbols of a corpus (phonemes, letters) into two subsets that correspond well with the phonetic definition of consonants and vowels; building around this result, the morphological component attempts to establish the list of roots and patterns in the corpus, and to infer the rules that govern their combinations. We assess the extent to which this can be done on the basis of two hypotheses: (i) the distinction between consonants and vowels can be learned by observing their tendency to alternate in speech; (ii) roots and patterns can be identified as sequences of the previously discovered consonants and vowels respectively. The proposed algorithm uses a purely distributional method for partitioning symbols. Then it applies analogical principles to identify a preliminary set of reliable roots and patterns, and gradually enlarge it. This extension process is guided by an evaluation procedure based on the minimum description length principle, in line with the approach to morphological learning embodied in LINGUISTICA (Goldsmith, 2001). The algorithm is implemented as a computer program named ARABICA; it is evaluated with regard to its ability to account for the system of plural formation in a corpus of Arabic nouns. This thesis shows that complex linguistic structures can be discovered without recourse to a rich set of a priori hypotheses about the phenomena under consideration. It illustrates the possible synergy between learning mechanisms operating at distinct levels of linguistic description, and attempts to determine where and why such a cooperation fails. It concludes that the tension between the universality of the consonant-vowel distinction and the specificity of root-and-pattern structure is crucial for understanding the advantages and weaknesses of this approach.

Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (~10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

Marketing education, distance learning and hypermedia : teaching 'current issues in marketing' in a virtual campus

This article reports on a project at the Universitat Oberta de Catalunya (UOC: The Open University of Catalonia, Barcelona) to develop an innovative package of hypermedia-based learning materials for a new course entitled 'Current Issues in Marketing'. The UOC is a distance university entirely based on a virtual campus. The learning materials project was undertaken in order to benefit from the advantages which new communication technologies offer to the teaching of marketing in distance education. The article reviews the main issues involved in incorporating new technologies in learning materials, the development of the learning materials, and their functioning within the hypermedia based virtual campus of the UOC. An empirical study is then carried out in order to evaluate the attitudes of students to the project. Finally, suggestions for improving similar projects in the future are put forward.

Improving Searching and Browsing Capabilities of Learning Object Repositories

Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.

Mentorointi ja sen kehittämismahdollisuudet S-ryhmässä

Tutkielman tavoitteena oli analysoida mentorointia, sen teoriaa ja menetelmää sekä miten sitä käytetään S-ryhmässä ja millä tavalla mentoroinnin käyttöä voidaan edistää johtamisen tukena S-ryhmässä. Tutkimuksessa tarkasteltiin mentorointia menetelmänä, jolla edistetään toisilta oppimista. Mentorointia analysoidaan myös hiljaisen tiedon, tietopääoman johtamisen ja kehittyvän johtajan ominaisuuksien näkökulmista. Osatavoitteiksi asetettiin kahdella eri tavalla toteutettujen mentorointiohjelmien erojen selvittäminen tuloksiltaan ja vaikutuksiltaan sekä mentoroinnin avulla tapahtuva oppimisen tehostaminen S-ryhmässä. Tutkielmassa käytetty konstruktiivinen metodologia valittiin tutkimusongelman perusteella, koska tarkoituksena oli kehittää S-ryhmään mentoroinnin hyödyntämiseen sopiva malli. Tiedonkeruumenetelmänä käytettiin sekä kyselytutkimusta että tietoverkossa tapahtuvaa strukturoitua teemahaastattelua. Tutkimuksen teoriaosa perustuu alan kirjallisuuteen, aihetta käsitteleviin kotimaisiin ja ulkomaisiin tutkimuksiin sekä tieteellisiin lehti- ja muihin artikkeleihin. Aikaisemman teoreettisen tutkimuksen perusteella laadittiin teoreettinen viitekehys, joka muodosti perustan tutkimuksen empiiriselle osalle. Empiirinen osa koostuu S-ryhmän mentorointiprojektia koskevasta materiaalista, mentorointiprojektiin osallistuneiden henkilöiden kyselytutkimuksista sekä S-ryhmän johtoon kuuluvien yhdeksän johtajan haastattelu-tutkimuksesta. Tutkimuksen päätuloksena oli se, että mentorointi sopii erinomaisen hyvin kehitysmenetelmäksi osaamisen ja kokemustiedon siirtämiseen vanhemmilta johtajilta nuorille potentiaalisille johtajille. Tämä tulos on erityisen merkittävä S-ryhmälle johtajaosaamisen kehittämisessä, kun ryhmään ollaan parhaillaan kasvattamassa uutta johtajasukupolvea. Empiirinen tutkimus tukee myös sitä näkemystä, että mentoroinnin toteuttamiseen S-ryhmässä on olemassa erilaisia tapoja. Keskeisenä tavoitteena toteutuksissa on henkilöiden kehittyminen ja oppiminen. Tutkimustuloksissa korostuivat myös nuoremman eli mentoroitavan tarve päästä kahdenkeskiseen, avoimeen ja luottamukselliseen keskusteluun kokeneemman henkilön kanssa. Näiden tutkimustulosten perusteella päädyttiin seuraaviin johtopäätöksiin: S-ryhmä tarvitsee oman mentorointijärjestelmän, joka toteutetaan ohjatun mentorointimallin mukaisesti. S-ryhmään on tärkeä perustaa oma mentorointipooli, jossa on halukkaita, eri osa-alueita osaavia mentoreita ja meklari, joka yhdyttää osapuolet toisiinsa.

Helping medical students to acquire a deeper understanding of truth-telling.

PROBLEM: Truth-telling is an important component of respect for patients' self-determination, but in the context of breaking bad news, it is also a distressing and difficult task. INTERVENTION: We investigated the long-term influence of a simulated patient-based teaching intervention, integrating learning objectives in communication skills and ethics into students' attitudes and concerns regarding truth-telling. We followed two cohorts of medical students from the preclinical third year to their clinical rotations (fifth year). Open-ended responses were analysed to explore medical students' reported difficulties in breaking bad news. CONTEXT: This intervention was implemented during the last preclinical year of a problem-based medical curriculum, in collaboration between the doctor-patient communication and ethics programs. OUTCOME: Over time, concerns such as empathy and truthfulness shifted from a personal to a relational focus. Whereas 'truthfulness' was a concern for the content of the message, 'truth-telling' included concerns on how information was communicated and how realistically it was received. Truth-telling required empathy, adaptation to the patient, and appropriate management of emotions, both for the patient's welfare and for a realistic understanding of the situation. LESSONS LEARNED: Our study confirms that an intervention confronting students with a realistic situation succeeds in making them more aware of the real issues of truth-telling. Medical students deepened their reflection over time, acquiring a deeper understanding of the relational dimension of values such as truth-telling, and honing their view of empathy.

Metadata for describing learning scenarios under European Higher Education Area paradigm

In this paper we identify the requirements for creating formal descriptions of learning scenarios designed under the European HigherEducation Area paradigm, using competences and learning activities as the basic pieces of the learning process, instead of contents and learning resources, pursuing personalization. Classical arrangements of content based courses are no longer enough to describe all the richness of this new learning process, where user profiles, competences and complex hierarchical itineraries need to be properly combined. We study the intersection with the current IMS Learning Design specification and theadditional metadata required for describing such learning scenarios. This new approach involves the use of case based learning and collaborativelearning in order to acquire and develop competences, following adaptive learning paths in two structured levels.