Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

The influence of learning styles, enrolment status and gender on academic performance of optometry undergraduates

Purpose: This cross-sectional study was designed to determine whether the academic performance of optometry undergraduates is influenced by enrolment status, learning style or gender. Methods: Three hundred and sixty undergraduates in all 3 years of the optometry degree course at Aston University during 2008–2009 were asked for their informed consent to participate in this study. Enrolment status was known from admissions records. An Index of Learning Styles (http://www4.nscu.edu/unity/lockers/users/f/felder/public/Learning-Styles.html) determined learning style preference with respect to four different learning style axes; active-reflective, sensing-intuitive, visual-verbal and sequential-global. The influence of these factors on academic performance was investigated. Results: Two hundred and seventy students agreed to take part (75% of the cohort). 63% of the sample was female. There were 213 home non-graduates (entrants from the UK or European Union without a bachelor’s degree or higher), 14 home graduates (entrants from the UK or European Union with a bachelor’s degree or higher), 28 international non-graduates (entrants from outside the UK or European Union without a bachelor’s degree or higher) and 15 international graduates (entrants from outside the UK or European Union with a bachelor’s degree or higher). The majority of students were balanced learners (between 48% and 64% across four learning style axes). Any preferences were towards active, sensing, visual and sequential learning styles. Of the factors investigated in this study, learning styles were influenced by gender; females expressed a disproportionate preference for the reflective and visual learning styles. Academic performance was influenced by enrolment status; international graduates (95% confidence limits: 64–72%) outperformed all other student groups (home non graduates, 60–62%; international non graduates, 55–63%) apart from home graduates (57–69%). Conclusion: Our research has shown that the majority of optometry students have balanced learning styles and, from the factors studied, academic performance is only influenced by enrolment status. Although learning style questionnaires offer suggestions on how to improve learning efficacy, our findings indicate that current teaching methods do not need to be altered to suit varying learning style preferences as balanced learning styles can easily adapt to any teaching style (Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. London, UK: Learning and Skills Research Centre, 2004).

O jogo como estratégia de aprendizagem rítmica: A ludicidade na exploração das fichas de exercícios rítmicos

Elaborado com vista à reflexão fundamentada acerca do percurso, das práticas educativas e das políticas de ensino, o presente trabalho pretende reunir um conjunto de saberes inerentes à atividade docente. Relacionando teorias e práticas no âmbito do ensino da música, a apresentação de uma filosofia de trabalho, congregando como eixo estruturante a prática musical supervisionada realizada no âmbito do ensino vocacional, apresenta-se como pano de fundo à análise dos vários conteúdos referentes à literatura específica inerente à prática pedagógica. Partindo da caracterização da escola do ensino vocacional de música, nos ensinos Básico e Secundário, a presente reflexão emerge no seguimento do pensamento articulado das várias áreas do saber através da apreciação, fundamentação e discussão das perspetivas de ensino vigentes ao longo do processo de estágio. Deste modo, será exposta toda a perspetiva de ensino por nós desenvolvida, partindo de uma linha temporal, tendo por princípio a descrição de um plano modelo, com o objetivo de podermos demonstrar a direção do nosso pensamento. Tendo como objetivo verificar a eficácia da abordagem ao jogo, como estratégia de ensino, na aprendizagem do ritmo, na disciplina de Formação Musical, partimos de uma investigação bibliográfica sobre a relação entre a escola tradicional e a escola moderna, o jogo e a motivação, e por último, a aprendizagem do ritmo sobre a perspetiva das atividades de aprendizagem sequencial, sustentadas por Edwin Gordon. O projeto de investigação aborda uma nova conceção do jogo, aliando o mesmo às atividades de leitura rítmicas.

WebQuest per a la Farmàcia Clínica i Atenció Farmacèutica : nova metodologia mitjançant moderna tecnologia

El projecte ha estat realitzat en la Unitat de Farmàcia Clínica i Farmacoteràpia de la Facultat de Farmàcia de la Universitat de Barcelona i ha estat a desenvolupar en dos anys. La finalitat del projecte ha estat oferir a l'alumne un material docent en suport digital, adaptat a una metodologia més creativa i de treball en grup, orientat a la millora de la seva formació, autonomia i rendiment acadèmic, en els aspectes relacionats amb la Farmàcia Clínica i Farmacoteràpia i l'Atenció Farmacèutica, i que consistirà en una primera aproximació a les directrius europees. Aquest material docent en suport digital són WebQuest (WQ) estructurades per temes i via Internet, la qual cosa possibilita un sistema dinàmic de fàcil retroalimentació i en constant actualització. Mitjançant la utilització d'aquest material docent es treballen aspectes com l'ús de metodologies docents centrades en l'alumne, autoaprenentatge a distància, aprenentatge seqüencial, treball en grup, participació activa i responsabilitat de l'alumne en el procés d'ensenyament aprenentatge i l'aproximació del mateix a la realitat professional entre altres, i tot això encaminat a promoure l'adaptació dels plans docents a l'Espai Europeu d'Educació Superior (EEES). En aquest sentit podem indicar que s'han elaborat cinc WQs, els títols del qual són Sistemes Personalitzats de Dosificació, Compliment terapèutic: el gran repte actual: informació al pacient, Compliment terapèutic: el gran repte actual: informació al professional sanitari, Dispensació activa en Diabetis mellitus tipus 2 i Dispensació activa en Hipertensión arterial. Cadascuna de les WQs elaborades consta dels apartats Introducció, Tasca, Procés, Recursos, Avaluació, Conclusió, Guia Didàctica i Crèdits. Les WQs estan allotjades en la pàgina web de la Unitat de Farmàcia Clínica i Farmacoteràpia, i a elles s'accedeix a través de l'adreça web http://www.ub.és/farcli/wp0.htm

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

Sequential asymmetric auctions with endogenous participation

In this paper we suggest a model of sequential auctions with endogenous participation where each bidder conjectures about the number of participants at each round. Then, after learning his value, each bidder decides whether or not to participate in the auction. In the calculation of his expected value, each bidder uses his conjectures about the number of participants for each possible subgroup. In equilibrium, the conjectured probability is compatible with the probability of staying in the auction. In our model, players face participation costs, bidders may buy as many objects as they wish and they are allowed to drop out at any round. Bidders can drop out at any time, but they cannot come back to the auction. In particular we can determine the number of participants and expected prices in equilibrium. We show that for any bidding strategy, there exists such a probability of staying in the auction. For the case of stochastically independent objects, we show that in equilibrium every bidder who decides to continue submits a bid that is equal to his value at each round. When objects are stochastically identical, we are able to show that expected prices are decreasing.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

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.

When Do We Learn to Cooperate? The Role of Social Learning in Social Dilemmas

In this paper, I look at the interaction between social learning and cooperative behavior. I model this using a social dilemma game with publicly observed sequential actions and asymmetric information about pay offs. I find that some informed agents in this model act, individually and without collusion, to conceal the privately optimal action. Because the privately optimal action is socially costly the behavior of informed agents can lead to a Pareto improvement in a social dilemma. In my model I show that it is possible to get cooperative behavior if information is restricted to a small but non-zero proportion of the population. Moreover, such cooperative behavior occurs in a finite setting where it is public knowledge which agent will act last. The proportion of cooperative agents within the population can be made arbitrarily close to 1 by increasing the finite number of agents playing the game. Finally, I show that under a broad set of conditions that it is a Pareto improvement on a corner value, in the ex-ante welfare sense, for an interior proportion of the population to be informed.

Adaptive search and information updating in sequential mate choice

Classical treatments of problems of sequential mate choice assume that the distribution of the quality of potential mates is known a priori. This assumption, made for analytical purposes, may seem unrealistic, opposing empirical data as well as evolutionary arguments. Using stochastic dynamic programming, we develop a model that includes the possibility for searching individuals to learn about the distribution and in particular to update mean and variance during the search. In a constant environment, a priori knowledge of the parameter values brings strong benefits in both time needed to make a decision and average value of mate obtained. Knowing the variance yields more benefits than knowing the mean, and benefits increase with variance. However, the costs of learning become progressively lower as more time is available for choice. When parameter values differ between demes and/or searching periods, a strategy relying on fixed a priori information might lead to erroneous decisions, which confers advantages on the learning strategy. However, time for choice plays an important role as well: if a decision must be made rapidly, a fixed strategy may do better even when the fixed image does not coincide with the local parameter values. These results help in delineating the ecological-behavior context in which learning strategies may spread.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

The role of organization-level factors on informal learning process

This study discusses the nature of informal learning process in business organizations, and the importance of different organization-level factors in this process. The purpose of this study is to understand the role of organization-level factors on informal learning process with three subquestions: how informal learning process takes place in business organizations, what organization-level factors affects informal learning process, and how informal learning process is affected by organizational-level factors. The theoretical background of this study includes literatures on the concept of informal learning, its process, and organization-level factors that can affect informal learning process. The empirical research has been conducted in this study by face-to-face interviews. The interviews were conducted between June and August 2015 in Dhaka, Bangladesh. Thirteen interviews were made with the employees from different hierarchical levels from four freight forwarding MNCs in Bangladesh. Constant comparative analysis has been used to process the collected data until reaching a level of saturation. The empirical research found that all the phases in an informal learning process are not linear and sequential, and the role of organization-level factors on each phase varies with the degree and nature of each factor. In addition, the results also revealed that all the organization-level factors do not interact with each other while playing their role on informal learning process. The findings of this study considerably extend our understanding of the important role of HRD, manager, colleague, culture, and work structure on informal learning process in the workplace. However, future research in different organizational contexts is required to generalize the findings of this study.

The role of organization-level factors on informal learning process

This study discusses the nature of informal learning process in business organizations, and the importance of different organization-level factors in this process. The purpose of this study is to understand the role of organization-level factors on informal learning process with three subquestions: how informal learning process takes place in business organizations, what organizationlevel factors affects informal learning process, and how informal learning process is affected by organizational-level factors. The theoretical background of this study includes literatures on the concept of informal learning, its process, and organization-level factors that can affect informal learning process. The empirical research has been conducted in this study by face-to-face interviews. The interviews were conducted between June and August 2015 in Dhaka, Bangladesh. Thirteen interviews were made with the employees from different hierarchical levels from four freight forwarding MNCs in Bangladesh. Constant comparative analysis has been used to process the collected data until reaching a level of saturation. The empirical research found that all the phases in an informal learning process are not linear and sequential, and the role of organization-level factors on each phase varies with the degree and nature of each factor. In addition, the results also revealed that all the organization-level factors do not interact with each other while playing their role on informal learning process. The findings of this study considerably extend our understanding of the important role of HRD, manager, colleague, culture, and work structure on informal learning process in the workplace. However, future research in different organizational contexts is required to generalize the findings of this study.

Anxiety and learning / response behaviour in the context of intellectual problem solving by young children

Twenty-eight grade four students were ca.tegorized as either high or low anxious subjects as per Gillis' Child Anxiety Scale (a self-report general measure). In determining impulsivity in their response tendencies, via Kagan's Ma.tching Familiar Figures Test, a significant difference between the two groups was not found to exist. Training procedures (verbal labelling plus rehearsal strategies) were introduced in modification of their learning behaviour on a visual sequential memory task. Significantly more reflective memory recall behaviour was noted by both groups as a result. Furthermore, transfer of the reflective quality of this learning strategy produced significantly less impulsive response behaviour for high and low anxious subjects with respect to response latency and for low anxious subjects with respect to response accuracy.