Дисертації: "Dynamics of learning"

1

Kapmeier, Florian. "Dynamics of interorganizational learning in learning alliances /." Frankfurt am Main [u.a.] : Lang, 2007. http://www.gbv.de/dms/zbw/525116672.pdf.

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2

Kulich, Martin. "Dynamic Template Adjustment in Continuous Keystroke Dynamics." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234927.

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Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.

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3

Paenke, Ingo. "Dynamics of evolution and learning." Karlsruhe Univ.-Verl. Karlsruhe, 2008. http://d-nb.info/989361233/04.

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4

Giannitsarou, Chryssi. "Macroeconomic dynamics and adaptive learning." Thesis, London Business School (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399325.

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5

Ribeiro, Andre Figueiredo. "Graph dynamics : learning and representation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34184.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
Includes bibliographical references (p. 58-60).
Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts?
(cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets.
by Andre Figueiredo Ribeiro.
S.M.

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6

Malfait, Nicole. "Characteristics of dynamics learning and generalization." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85577.

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In order to grasp an object, the human nervous system must transform the intended hand displacement into control signals distributed to motor neurons and ultimately to muscles. The aim of this thesis is to describe the nature of the internal representations that the human motor system uses to perform reaching movements.
The aim of the first study was to provide a clear and simple way to test whether dynamical information is coded by the nervous system in an extrinsic, Cartesian, versus intrinsic, muscle- or joint-based, system of coordinates. As a means to determine the frame for reference used by the motor system, we examined how adaptation to externally applied forces transfers across different arm configurations. We trained subjects to make reaching movements while holding a robotic arm that applied forces proportional and perpendicular to the tangential velocity of the hand. While in the first trials hand paths were substantially deviated, subjects rapidly adapted to the new dynamic condition; they learned to compensate for the forces in order to restore the kinematics observed in the absence of load. Learning of the new dynamics transferred across movements performed in different regions of the workspace when the relation between joint displacements and experienced torques remained unchanged, rather than when the mapping between hand displacements and forces was preserved. This provided support to the idea that dynamics are encoded in muscle- or joint-based coordinates.
The results of the first study described a process of generalization that relies on the invariance of the mapping between torques and joint displacements. While this clearly points to an intrinsic coding of dynamics, it does not explain whether or how generalization over the workspace occurs when the pattern of torques changes with the configuration of the arm. In the second study, subjects learned a force field in which the forces acted always in the same direction relative to an external frame of reference, which defines a mapping between joint displacements and torques that varies with the configuration of the arm. Our idea was to test if in the absence of invariance in the pattern of torques, generalization would occur on the basis of the invariance in the direction of the forces represented in an extrinsic system of coordinates. (Abstract shortened by UMI.)

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7

Kim, Young Se. "Expectations, learning, and exchange rate dynamics." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1087229892.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xiii, 121 p.; also includes graphics (some col.) Includes bibliographical references (p. 117-121). Available online via OhioLINK's ETD Center

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8

Pradelski, Bary S. R. "Distributed dynamics and learning in games." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:37185594-633c-4d78-a408-dfe4978bacb7.

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In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where exchanges are frequent and the sheer size of the market hinders participants from learning about others' preferences. We consider learning dynamics that are based on trial-and-error and aspiration-based heuristics. Players occasionally try to increase their performance given their current payoffs. If successful they stick to the new action, otherwise they revert to their old action. We also study a dynamic model of social influence based on findings in sociology and psychology that people have a propensity to conform to others' behavior irrespective of the payoff consequences. We analyze the dynamics with a particular focus on two questions: How long does it take to reach equilibrium and what are the stability and welfare properties of the equilibria that the process selects? These questions are at the core of understanding which equilibrium concepts are robust in environments where players have little information about the game and the high rationality assumptions of standard game theory are not very realistic. Methodologically, this thesis builds on game theoretic techniques and prominent solution concepts such as the Nash equilibrium for non-cooperative games and the core for cooperative games, as well as refinement concepts like stochastic stability. The proofs rely on mathematical techniques from random walk theory and integer programming.

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9

Zhan, Beibei. "Learning crowd dynamics using computer vision." Thesis, Kingston University, 2008. http://eprints.kingston.ac.uk/20302/.

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An increase of violence in public spaces has prompted the introduction of more sophisticated technology to improve the safety and security of very crowded environments. Research disciplines such as civil engineering and sociology, have studied the crowd phenomenon for years, employing human visual observation to estimate the characteristics of a crowd. Computer vision researchers have increasingly been involved in the study and development of research methods for the automatic analysis of the crowd phenomenon. Until recently, most existing methods in computer vision have been focussed on extracting a limited number of features in controlled environments, with limited clutter and numbers of people. The main goal of this thesis is to advance the state of the art in computer vision methods for use in very crowded and cluttered environments. One of the aims is to devise a method that in the near future would be of help in other disciplines such as socio-dynamics and computer animation, where models of crowded scenes are built manually on painstaking visual observation. A series of novel methods is presented here that can learn crowd dynamics automatically by extracting different crowd information from real world crowded scenes and modelling crowd dynamics using computer vision. The developed methods include an individual behaviour classifier, a scene cluttering level estimator, two people counting schemes based on colour modelling and tracking, two algorithm for measuring crowd motion by matching local descriptors, and two dynamics modelling methods - one based on statistical techniques and the other one based on a neural network. The proposed information extracting methods are able to gather both macro information, which represents the properties of the whole crowd, and micro information, which is different from individual (location) to individual (location). The statistically-based dynamics modelling models the scene implicitly. Furthermore, a method for discovering the main path of the crowded scene is developed based on it. Self-Organizing Map (SOM) is chosen in the neural network approach of modelling dynamics; the resulting SOMs are proven to be able to capture the main dynamics of the crowded scene.

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10

Shah, Jagesh V. (Jagesh Vijaykumar). "Learning dynamics in feedforward neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36541.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 108-115).
by Jagesh V. Shah.
M.S.

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11

Izquierdo, Eduardo J. "The dynamics of learning behaviour : a situated, embodied, and dynamical systems approach." Thesis, University of Sussex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488595.

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12

Heimel, Jan-Alexander Frank. "Dynamics of learning by neurons and agents." Thesis, King's College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252140.

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13

Christev, Atanas Christev. "On the dynamics of hyperinflation and learning /." Search for this dissertation online, 2003. http://wwwlib.umi.com/cr/ksu/main.

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14

Xie, Xiaohui 1972. "Dynamics and learning in recurrent neural networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8393.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.
Includes bibliographical references (p. 141-151).
This thesis is a study of dynamics and learning in recurrent neural networks. Many computations of neural systems are carried out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the network's function. In this thesis, I aim at studying several recurrent network models and relating the dynamics with the networks' computation. For this purpose, three systems are studied and analyzed in detail: The first one is a network model for direction selectivity; the second one is a generalized network of Winner-Take-All; the third one is a model for integration in head-direction systems. One distinctive feature of neural systems is the ability of learning. The other part of my thesis is on learning in biologically motivated neural networks. Specifically, I study how the spike-time-dependent synaptic plasticity helps to stabilize persistent neural activities in the ocular motor integrator. I study the connections between back-propagation and contrastive-Hebbian learning, and show how backpropagation could be equivalently implemented by contrastive-Hebbian learning in a layered network. I also propose a learning rule governing synaptic plasticity in a network of spiking neurons and compare it with recent experimental results on spike-time-dependent plasticity.
by Xiaohui Xie.
Ph.D.

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15

Deshpande, Ashwin. "Learning probabilistic relational dynamics for multiple tasks." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41648.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 57-58).
While large data sets have enabled machine learning algorithms to act intelligently in complex domains, standard machine learning algorithms perform poorly in situations in which little data exists for the desired target task. Transfer learning attempts to extract trends from the data of similar source tasks to enhance learning in the target task. We apply transfer learning to probabilistic rule learning to learn the dynamics of a target world. We utilize a hierarchical Bayesian framework and specify a generative model which dictates the probabilities of task data, task rulesets and a common global ruleset. Through a greedy coordinated-ascent algorithm, the source tasks contribute towards building the global ruleset which can then be used as a prior to supplement the data from the target ruleset. Simulated experimental results in a variety of blocks-world domains suggest that employing transfer learning can provide significant accuracy gains over traditional single task rule learning algorithms.
by Ashwin Deshpande.
M.Eng.

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16

Johnson, Matthew J. Ph D. Massachusetts Institute of Technology (Matthew James). "Bayesian nonparametric learning with semi-Markovian dynamics." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60170.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Includes bibliographical references (p. 65-66).
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDPHMM to capture such structure by drawing upon explicit-duration semi-Markovianity, which has been developed in the parametric setting to allow construction of highly interpretable models that admit natural prior information on state durations. In this thesis we introduce the explicit-duration Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM) and develop posterior sampling algorithms for efficient inference. We also develop novel sampling inference for the Bayesian version of the classical explicit-duration Hidden semi-Markov Model. We demonstrate the utility of the HDP-HSMM and our inference methods on synthetic data as well as experiments on a speaker diarization problem and an example of learning the patterns in Morse code.
by Matthew J Johnson.
S.M.

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17

Adrian, Tobias 1971. "Learning, dynamics of beliefs, and asset pricing." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17571.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2003.
Includes bibliographical references (p. 130-139).
In the first chapter, I study the impact of statistical arbitrage on equilibrium asset prices. Arbitrageurs have to learn about the long-run behavior of the stock price process. They condition their investment strategy on the observation of price and volume. The learning process of the statistical arbitrageurs leads to an optimal trading strategy that can be upward sloping in prices. The presence of privately informed investors makes the equilibrium price dependent on the history of trading volume. The response of prices to news is nonlinear, and little news can have large effects in some ranges of the prices. In the second chapter, together with Francesco Franzoni, we develop an equilibrium model of learning about time-varying risk factor loadings. In the model, CAPM holds from investors' ex-ante perspective. However, positive mispricing can be observed when investors' expectations of beta are above ex-post realizations. This model is used to explain the 'value premium'. In a learning framework, the fact that value stocks used to be more risky in the past leads to investors' expectations of beta that exceed the estimates from more recent samples. We propose an empirical methodology that takes investors' expectations of the factor loadings explicitly into account when estimating betas. With the adjusted estimates of beta, we can explain the cross-section of average returns of the ten book-to-market portfolios, and account for the value premium in the relevant sample. The third chapter investigates the role of contagion during the Great Depression. The Great Depression was a worldwide phenomenon, accompanied by financial crisis. I investigate whether financial contagion contributed to the spread of the Great Depression across countries. Contagion happens when idiosyncratic shocks are transmitted from one country to another.
(cont.) Asset price movements in the country affected by contagion are not justified by its own fundamentals. Contagion leads to an increase in the covariance of international financial markets during periods of financial crisis. Two particular events are tested: the stock market crash of 1929 and the Latin American debt crises of 1931. In both events the hypothesis that the crises spread contagiously is rejected with one exception: the French Stock Market.
by Tobias Adrian.
Ph.D.

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18

Grasselli, Nora Ilona. "MBA learning group dynamics : Structures and processes." Jouy-en Josas, HEC, 2008. http://www.theses.fr/2008EHEC0010.

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Cette thèse vise à explorer la dynamique de petits groupes d’apprentissage autogérés et non-hiérarchiques au sein d’un programme MBA. A travers un processus inspiré de la recherche-action et de la psychosociologie, cette recherche se focalise d’abord sur deux hypothèses de travail : l’impact de la iversité et la fonction des espaces sociaux dans ces groupes. Cependant, cette recherche a permis de mettre en évidence que la question centrale dans ces groupes d’apprentissage serait leur design : la division du travail appropriée, la gestion du temps, et l’allocation des rôles. Les analyses complémentaires concernant la division du travail et la gestion du temps montrent que ces éléments du design peuvent aussi fonctionner comme stratégies protectrices contre les difficultés possibles que ces groupes d’apprentissage rencontrent pendant leur fonctionnement. Ainsi, cette recherche met en avant l’importance d’un design adaptatif et ses liens aves les processus internes dans les petits groupes. Cette thèse met aussi en évidence l’importance de la démarche de recherche-action, du moins de son esprit, qui permet de découvrir des phénomènes subtils et imprévus et de répondre aux critiques formulées à l’encontre des MBA, stigmatisant la normalisation des apprentissages et des comportements
This study explores the dynamics of small, non-hierarchical, self-managing learning groups in an MBA program. In the spirit of action research and psychosociology two initial working hypotheses, the impact of diversity on the groups and the use of social spaces, are examined. Nonetheless, it turns out that the central issue in the learning groups seems to be the groups’ design, e. G. The adequate division of labor, the management of time, and the allocation of roles. Further analyses on labor division and group time management show that these design features may also function as protective strategies against the possible difficulties the learning groups risk to encounter. Herewith this research puts forward the importance of adaptive group designs and their links with the internal processes in small groups. This study also emphasizes the value of action research for discovering subtle, unpredictable phenomena and for providing a possible response to the critiques addressed to the standardized learning and behaviors on MBA programs

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19

Burkov, Andriy. "Adaptive Dynamics Learning and Q-initialization in the Context of Multiagent Learning." Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24476/24476.pdf.

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L’apprentissage multiagent est une direction prometteuse de la recherche récente et à venir dans le contexte des systèmes intelligents. Si le cas mono-agent a été beaucoup étudié pendant les deux dernières décennies, le cas multiagent a été peu étudié vu sa complexité. Lorsque plusieurs agents autonomes apprennent et agissent simultanément, l’environnement devient strictement imprévisible et toutes les suppositions qui sont faites dans le cas mono-agent, telles que la stationnarité et la propriété markovienne, s’avèrent souvent inapplicables dans le contexte multiagent. Dans ce travail de maîtrise nous étudions ce qui a été fait dans ce domaine de recherches jusqu’ici, et proposons une approche originale à l’apprentissage multiagent en présence d’agents adaptatifs. Nous expliquons pourquoi une telle approche donne les résultats prometteurs lorsqu’on la compare aux différentes autres approches existantes. Il convient de noter que l’un des problèmes les plus ardus des algorithmes modernes d’apprentissage multiagent réside dans leur complexité computationnelle qui est fort élevée. Ceci est dû au fait que la taille de l’espace d’états du problème multiagent est exponentiel en le nombre d’agents qui agissent dans cet environnement. Dans ce travail, nous proposons une nouvelle approche de la réduction de la complexité de l’apprentissage par renforcement multiagent. Une telle approche permet de réduire de manière significative la partie de l’espace d’états visitée par les agents pour apprendre une solution efficace. Nous évaluons ensuite nos algorithmes sur un ensemble d’essais empiriques et présentons des résultats théoriques préliminaires qui ne sont qu’une première étape pour former une base de la validité de nos approches de l’apprentissage multiagent.
Multiagent learning is a promising direction of the modern and future research in the context of intelligent systems. While the single-agent case has been well studied in the last two decades, the multiagent case has not been broadly studied due to its complex- ity. When several autonomous agents learn and act simultaneously, the environment becomes strictly unpredictable and all assumptions that are made in single-agent case, such as stationarity and the Markovian property, often do not hold in the multiagent context. In this Master’s work we study what has been done in this research field, and propose an original approach to multiagent learning in presence of adaptive agents. We explain why such an approach gives promising results by comparing it with other different existing approaches. It is important to note that one of the most challenging problems of all multiagent learning algorithms is their high computational complexity. This is due to the fact that the state space size of multiagent problem is exponential in the number of agents acting in the environment. In this work we propose a novel approach to the complexity reduction of the multiagent reinforcement learning. Such an approach permits to significantly reduce the part of the state space needed to be visited by the agents to learn an efficient solution. Then we evaluate our algorithms on a set of empirical tests and give a preliminary theoretical result, which is first step in forming the basis of validity of our approaches to multiagent learning.

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20

Mattar, Andrew A. G. "On the retention of learned dynamics." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84060.

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When one learns a novel motor skill, retention of that skill requires consolidation of motor learning. Previous reports have shown that preceding sessions of motor learning can interfere with the acquisition of new tasks and that new motor learning can disrupt previously retained skills. A recent study by Caithness et al. (2004) shows that new learning, even after long delays, can totally disrupt prior retention. This finding is consistent with the idea that re-activated memories become labile and subject to displacement. However the result is difficult to reconcile with day-to-day experience in which skills improve with repetition and are not disrupted by unrelated activities. In this experiment, we show that when subjects learn new dynamics the influence of one task on another depends on the similarity of the force fields involved. We used a robotic manipulandum to define environments in which subjects learned to move. We used an AB design in which subjects learned field A on day one and B on day 2. We show that the effect of having learned environment A 24-hours prior to learning B varies along a continuum from facilitation when they are identical, through little effect when they are unrelated, to total interference when they are opposite. These findings thus indicate that the nervous system encodes information about dynamics in a fashion that is predictable on the basis of the similarity between the initial and final training environments. One month following their initial training, we tested subjects environment C, whose dynamics were opposite to B. Performance on this task suggests that the nervous system retained neither discrete instances of past training nor solely the most recent motor learning, but instead constructed a running average of learned dynamics to build an individual's motor repertoire.

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21

Edenhammar, Clara. "The dynamics of the case method: A comparative study." Thesis, Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-32997.

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22

Paenke, Ingo [Verfasser]. "Dynamics of evolution and learning / by Ingo Paenke." Karlsruhe : Univ.-Verl. Karlsruhe, 2008. http://d-nb.info/98966662X/34.

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23

Richardson, Christine, and Maggie Exon. "Managing discussion group dynamics in e-learning environments." School of Communication & Information, Nanyang Technological University, 2006. http://hdl.handle.net/10150/105260.

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This paper examines the challenges involved in understanding group dynamics when utilizing online teaching platforms, such as WebCT. When the student cohort involved is studying professionally oriented technical subjects, people who have prior knowledge of this professional area may exhibit be-haviors which overwhelm other students. In traditional face-to-face tutorial and workshop environments, teachers are able to interact with students, ensuring that they have a comfortable environment in which to contribute and learn. This may involve bringing them out of themselves when they appear intimidated and re-directing them when they threaten to dominate. Teachers can interpret body language and use their own body language and facial expressions as well as verbal comment to maintain a student-centered learning environment. It is much more difficult to influence the dynamics of online discussion. Our ex-perience at Curtin has shown that the potential for intimidation of students leading to their non-participation is stronger than in the classroom, especially when prior professional knowledge and experi-ence is involved. This outcome is opposite to expectations of web communication, usually believed to be an environment where people can overcome the constraints of their personality and participate in discus-sion more easily. Reasons this may occur will be examined together with techniques for ensuring that students are afforded an equitable learning environment.

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24

Ong, Eng-Jon. "Learning the visual dynamics of human body motions." Thesis, Queen Mary, University of London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270937.

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25

Petkos, Georgios. "Learning dynamics for robot control under varying contexts." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3130.

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High fidelity, compliant robot control requires a sufficiently accurate dynamics model. Often though, it is not possible to obtain a dynamics model sufficiently accurately or at all using analytical methods. In such cases, an alternative is to learn the dynamics model from movement data. This thesis discusses the problems specific to dynamics learning for control under nonstationarity of the dynamics. We refer to the cause of the nonstationarity as the context of the dynamics. Contexts are, typically, not directly observable. For instance, the dynamics of a robot manipulator changes as the robot manipulates different objects and the physical properties of the load – the context of the dynamics – are not directly known by the controller. Other examples of contexts that affect the dynamics are changing force fields or liquids with different viscosity in which a manipulator has to operate. The learned dynamics model needs to be adapted whenever the context and therefore the dynamics changes. Inevitably, performance drops during the period of adaptation. The goal of this work, is to reuse and generalize the experience obtained by learning the dynamics of different contexts in order to adapt to changing contexts fast. We first examine the case that the dynamics may switch between a discrete, finite set of contexts and use multiple models and switching between them to adapt the controller fast. A probabilistic formulation of multiple models is used, where a discrete latent variable is used to represent the unobserved context and index the models. In comparison to previous multiple model approaches, the developed method is able to learn multiple models of nonlinear dynamics, using an appropriately modified EM algorithm. We also deal with the case when there exists a continuum of possible contexts that affect the dynamics and hence, it becomes essential to generalize from a set of experienced contexts to novel contexts. There is very little previous work on this direction and the developed methods are completely novel. We introduce a set of continuous latent variables to represent context and introduce a dynamics model that depends on this set of variables. We first examine learning and inference in such a model when there is strong prior knowledge on the relationship of these continuous latent variables to the modulation of the dynamics, e.g., when the load at the end effector changes. We also develop methods for the case that there is no such knowledge available. Finally, we formulate a dynamics model whose input is augmented with observed variables that convey contextual information indirectly, e.g., the information from tactile sensors at the interface between the load and the arm. This approach also allows generalization to not previously seen contexts and is applicable when the nature of the context is not known. In addition, we show that use of such a model is possible even when special sensory input is not available by using an instance of an autoregressive model. The developed methods are tested on realistic, full physics simulations of robot arm systems including a simplistic 3 degree of freedom (DOF) arm and a simulation of the 7 DOF DLR light weight robot arm. In the experiments, varying contexts are different manipulated objects. Nevertheless, the developed methods (with the exception of the methods that require prior knowledge on the relationship of the context to the modulation of the dynamics) are more generally applicable and could be used to deal with different context variation scenarios.

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26

Schoenberg, Uta. "Learning, mobility and wage dynamics : theory and evidence." Thesis, University College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406249.

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27

Guse, Eran A. "Essays on heterogeneity, learning dynamics, and aggregate fluctuations /." view abstract or download file of text, 2003. http://wwwlib.umi.com/cr/uoregon/fullcit?p3095249.

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Thesis (Ph. D.)--University of Oregon, 2003.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 139-142). Also available for download via the World Wide Web; free to University of Oregon users.

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28

Karlsson, Mattias P. "Network dynamics in the hippocampus during spatial learning." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3324622.

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29

Trifonova, Neda. "Machine-learning approaches for modelling fish population dynamics." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13386.

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Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. Understanding the nature of functional relationships (such as prey-predator) between species is important for building predictive models. However, modelling the interactions with external stressors over time and space is also essential for ecosystem-based approaches to fisheries management. With the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, fewer assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data and combined with existing knowledge. In this thesis, we explore Bayesian network modelling approaches, accounting for latent effects to reveal species dynamics within geographically different marine ecosystems. First, we introduce the concept of functional equivalence between different fish species and generalise trophic structure from different marine ecosystems in order to predict influence from natural and anthropogenic sources. The importance of a hidden variable in fish community change studies of this nature was acknowledged because it allows causes of change which are not purely found within the constrained model structure. Then, a functional network modelling approach was developed for the region of North Sea that takes into consideration unmeasured latent effects and spatial autocorrelation to model species interactions and associations with external factors such as climate and fisheries exploitation. The proposed model was able to produce novel insights on the ecosystem's dynamics and ecological interactions mainly because it accounts for the heterogeneous nature of the driving factors within spatially differentiated areas and their changes over time. Finally, a modified version of this dynamic Bayesian network model was used to predict the response of different ecosystem components to change in anthropogenic and environmental factors. Through the development of fisheries catch, temperature and productivity scenarios, we explore the future of different fish and zooplankton species and examine what trends of fisheries exploitation and environmental change are potentially beneficial in terms of ecological stability and resilience. Thus, we were able to provide a new data-driven modelling approach which might be beneficial to give strategic advice on potential response of the system to pressure.

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30

Verri, Filipe Alves Neto. "Collective dynamics in complex networks for machine learning." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18102018-113054/.

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Machine learning enables machines to learn automatically from data. In literature, graph-based methods have received increasing attention due to their ability to learn from both local and global information. In these methods, each data instance is represented by a vertex and is linked to other vertices according to a predefined affinity rule. However, they usually have unfeasible time cost for large problems. To overcome this problem, techniques can employ a heuristic to find suboptimal solutions in a feasible time. Early heuristic optimization methods exploit nature-inspired collective processes, such as ants looking for food sources and swarms of bees. Nowadays, advances in the field of complex systems provide powerful tools to assess and to understand dynamical systems. Complex networks, which are graphs with nontrivial topology, are among these theoretical tools capable of describing the interplay of topology, structure, and dynamics of complex systems. Therefore, machine learning methods based on complex networks and collective dynamics have been proposed. They encompass three steps. First, a complex network is constructed from the input data. Then, the simulation of a distributed collective system in the network generates rich information. Finally, the collected information is used to solve the learning problem. The coordination of the individuals in the system permit to achieve dynamics that is far more complex than the behavior of single individuals. In this research, I have explored collective dynamics in machine learning tasks, both in unsupervised and semi-supervised scenarios. Specifically, I have proposed a new collective system of competing particles that shifts the traditional vertex-centric dynamics to a more informative edge-centric one. Moreover, it is the first particle competition system applied in machine learning task that has deterministic behavior. Results show several advantages of the edge-centric model, including the ability to acquire more information about overlapping areas, a better exploration behavior, and a faster convergence time. Also, I have proposed a new network formation technique that is not based on similarity and has low computational cost. Since addition and removal of samples in the network is cheap, it can be used in real-time application. Finally, I have conducted analytical investigations of a flocking-like system that was needed to guarantee the expected behavior in community detection tasks. In conclusion, the result of the research contributes to many areas of machine learning and complex systems.
Aprendizado de máquina permite que computadores aprendam automaticamente dos dados. Na literatura, métodos baseados em grafos recebem crescente atenção por serem capazes de aprender através de informações locais e globais. Nestes métodos, cada item de dado é um vértice e as conexões são dadas uma regra de afinidade. Todavia, tais técnicas possuem custo de tempo impraticável para grandes grafos. O uso de heurísticas supera este problema, encontrando soluções subótimas em tempo factível. No início, alguns métodos de otimização inspiraram suas heurísticas em processos naturais coletivos, como formigas procurando por comida e enxames de abelhas. Atualmente, os avanços na área de sistemas complexos provêm ferramentas para medir e entender estes sistemas. Redes complexas, as quais são grafos com topologia não trivial, são uma das ferramentas. Elas são capazes de descrever as relações entre topologia, estrutura e dinâmica de sistemas complexos. Deste modo, novos métodos de aprendizado baseados em redes complexas e dinâmica coletiva vêm surgindo. Eles atuam em três passos. Primeiro, uma rede complexa é construída da entrada. Então, simula-se um sistema coletivo distribuído na rede para obter informações. Enfim, a informação coletada é utilizada para resolver o problema. A interação entre indivíduos no sistema permite alcançar uma dinâmica muito mais complexa do que o comportamento individual. Nesta pesquisa, estudei o uso de dinâmica coletiva em problemas de aprendizado de máquina, tanto em casos não supervisionados como semissupervisionados. Especificamente, propus um novo sistema de competição de partículas cuja competição ocorre em arestas ao invés de vértices, aumentando a informação do sistema. Ainda, o sistema proposto é o primeiro modelo de competição de partículas aplicado em aprendizado de máquina com comportamento determinístico. Resultados comprovam várias vantagens do modelo em arestas, includindo detecção de áreas sobrepostas, melhor exploração do espaço e convergência mais rápida. Além disso, apresento uma nova técnica de formação de redes que não é baseada na similaridade dos dados e possui baixa complexidade computational. Uma vez que o custo de inserção e remoção de exemplos na rede é barato, o método pode ser aplicado em aplicações de tempo real. Finalmente, conduzi um estudo analítico em um sistema de alinhamento de partículas. O estudo foi necessário para garantir o comportamento esperado na aplicação do sistema em problemas de detecção de comunidades. Em suma, os resultados da pesquisa contribuíram para várias áreas de aprendizado de máquina e sistemas complexos.

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31

Tolson, Edward (Edward Thomas) 1980. "Learning models of world dynamics using Bayesian networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87841.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 72-74).
by Edward Tolson.
M.Eng.

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32

Renner, Michael Robert. "Machine Learning Simulation: Torso Dynamics of Robotic Biped." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/34602.

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Military, Medical, Exploratory, and Commercial robots have much to gain from exchanging wheels for legs. However, the equations of motion of dynamic bipedal walker models are highly coupled and non-linear, making the selection of an appropriate control scheme difficult. A temporal difference reinforcement learning method known as Q-learning develops complex control policies through environmental exploration and exploitation. As a proof of concept, Q-learning was applied through simulation to a benchmark single pendulum swing-up/balance task; the value function was first approximated with a look-up table, and then an artificial neural network. We then applied Evolutionary Function Approximation for Reinforcement Learning to effectively control the swing-leg and torso of a 3 degree of freedom active dynamic bipedal walker in simulation. The model began each episode in a stationary vertical configuration. At each time-step the learning agent was rewarded for horizontal hip displacement scaled by torso altitude--which promoted faster walking while maintaining an upright posture--and one of six coupled torque activations were applied through two first-order filters. Over the course of 23 generations, an approximation of the value function was evolved which enabled walking at an average speed of 0.36 m/s. The agent oscillated the torso forward then backward at each step, driving the walker forward for forty-two steps in thirty seconds without falling over. This work represents the foundation for improvements in anthropomorphic bipedal robots, exoskeleton mechanisms to assist in walking, and smart prosthetics.
Master of Science

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33

Bajic, Daniel Andrew. "The temporal dynamics of strategy execution in cognitive skill learning." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3369155.

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Thesis (Ph. D.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed September 15, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.

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34

Georgette, Jeffrey Phillip. "Active Learning using Model-Eliciting Activities and Inquiry-Based Learning Activities in Dynamics." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1117.

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This thesis focuses on a year-long project of implementing active learning in undergraduate dynamics courses at Cal Poly San Luis Obispo from 2012-2013. The purpose is to increase conceptual understanding of critical dynamics concepts and to repair misconceptions of the students. Conceptual understanding in Dynamics is vital to understanding the big picture, building upon previous knowledge, and better understanding the behavior of engineering systems. Through various hands-on activities, students make predictions, test their conceptions, and solve real world problems. These active learning methods allow students to improve their learning of Dynamics concepts. Education research on active learning is present in Physics and Mathematics disciplines, yet is still growing in Engineering. Four Inquiry-Based Learning Activities (IBLAs) and two Model-Eliciting Activities (MEAs) are discussed in this thesis. Inquiry-Based Learning Activities feature student prediction and experimentation in which the physical world acts as the authority. On the other hand, Model-Eliciting-Activities prompt students to solve real world problems and deliver results to a client. From the results, some activities yield an increase in conceptual understanding, as measured by assessment items, while others do not yield a significant increase. These activities not only help to promote conceptual gains, but also to motivate students and offer realistic engineering contexts. In conclusion, the six total IBLA and MEAS will continue in practice and be improved in their implementation. This thesis work will contribute to engineering education research of active learning methods, and improve the undergraduate dynamics curriculum locally at Cal Poly.

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Pérez, Culubret Adrià 1993. "Learning how to simulate : Applying machine learning methods to improve molecular dynamics simulations." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2022. http://hdl.handle.net/10803/673392.

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Caracteritzar la dinàmica de les proteïnes és essencial per tal d'entendre la connexió entre seqüència i funció. La simulació de dinàmiques moleculars és una de les tècniques principals per a estudiar la dinàmica de proteïnes per la seva capacitat de capturar processos dinàmics computacionals en diferents escales temporals amb resolució atòmica. Tanmateix, hi ha limitacions que impedeixen que la simulació de dinàmiques moleculars es converteixi en un model substitutiu de les dinàmiques reals de proteïnes, principalment per limitacions de mostreig i la inexactitud dels camps de força utilitzats. En aquesta tesi doctoral tractem aquestes limitacions mitjançant els últims avenços en aprenentatge automàtic. En la primera part de la tesi, desenvoluparem un nou algoritme de mostreig adaptatiu inspirat en mètodes d'aprenentatge reforçat, que aplicarem per a reconstruir la unió entre una proteïna desordenada i la seva parella d'unió. En la segona part de la tesi, desenvoluparem TorchMD, una llibreria d'aprenentatge profund per a simulacions de dinàmica molecular, que aplicarem per a aprendre un potencial "coarse-grained" per a simulacions de plegament de proteïnes.

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36

Berger, Ulrich. "Learning to trust, learning to be trustworthy." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4806/1/wp212.pdf.

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Interpersonal trust is a one-sided social dilemma. Building on the binary trust game, we ask how trust and trustworthiness can evolve in a population where partners are matched randomly and agents sometimes act as trustors and sometimes as trustees. Trustors have the option to costly check a trustee's last action and to condition their behavior on the signal they receive. We show that the resulting population game admits two components of Nash equilibria. Nevertheless, the long-run outcome of an evolutionary social learning process modeled by the best response dynamics is unique. Even if unconditional distrust initially abounds, the trustors' checking option leads trustees to build a reputation for trustworthiness by honoring trust. This invites free-riders among the trustors who save the costs of checking and trust blindly, until it does no longer pay for trustees to behave in a trustworthy manner. This results in cyclical convergence to a mixed equilibrium with behavioral heterogeneity where suspicious checking and blind trusting coexist while unconditional distrust vanishes. (author's abstract)
Series: Department of Economics Working Paper Series

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37

Marsden, Christopher J. "Nonlinear dynamics of pattern recognition and optimization." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/10694.

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We associate learning in living systems with the shaping of the velocity vector field of a dynamical system in response to external, generally random, stimuli. We consider various approaches to implement a system that is able to adapt the whole vector field, rather than just parts of it - a drawback of the most common current learning systems: artificial neural networks. This leads us to propose the mathematical concept of self-shaping dynamical systems. To begin, there is an empty phase space with no attractors, and thus a zero velocity vector field. Upon receiving the random stimulus, the vector field deforms and eventually becomes smooth and deterministic, despite the random nature of the applied force, while the phase space develops various geometrical objects. We consider the simplest of these - gradient self-shaping systems, whose vector field is the gradient of some energy function, which under certain conditions develops into the multi-dimensional probability density distribution of the input. We explain how self-shaping systems are relevant to artificial neural networks. Firstly, we show that they can potentially perform pattern recognition tasks typically implemented by Hopfield neural networks, but without any supervision and on-line, and without developing spurious minima in the phase space. Secondly, they can reconstruct the probability density distribution of input signals, like probabilistic neural networks, but without the need for new training patterns to have to enter the network as new hardware units. We therefore regard self-shaping systems as a generalisation of the neural network concept, achieved by abandoning the "rigid units - flexible couplings'' paradigm and making the vector field fully flexible and amenable to external force. It is not clear how such systems could be implemented in hardware, and so this new concept presents an engineering challenge. It could also become an alternative paradigm for the modelling of both living and learning systems. Mathematically it is interesting to find how a self shaping system could develop non-trivial objects in the phase space such as periodic orbits or chaotic attractors. We investigate how a delayed vector field could form such objects. We show that this method produces chaos in a class systems which have very simple dynamics in the non-delayed case. We also demonstrate the coexistence of bounded and unbounded solutions dependent on the initial conditions and the value of the delay. Finally, we speculate about how such a method could be used in global optimization.

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38

Mattar, Andrew. "Generalization of dynamics learning across direction, distance and time." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86872.

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Humans demonstrate the ability to move with little error in a wide range of situations. To do so requires a high degree of plasticity in the neural control of movement. The study of dynamics learning, the process by which the nervous system learns neural signals that are needed to produce the forces underlying movement, is a well-known method for examining plasticity in motor control. This dissertation reports on a series of experiments on dynamics learning and its generalization. Our studies touch on two important features of the dynamics learning process. The first is that dynamics learning involves instances of local adaptation. The second is that generalization can occur based on interpolation between these instances of learning.
Following a General Introduction in Chapter 1, in Chapter 2 we examine whether having learned to move in multiple directions affects the extent of generalization. In Chapter 3, we examine generalization of dynamics learning across changes in movement amplitude. In Chapter 4, we note that generalization of learning does not depend only on the separation between training and test directions, as was shown in Chapter 2. In addition, generalization depends on the extent of learning during training. We examine the effect of impedance - a mechanical property of the arm under neural control - on dynamics learning and generalization. In Chapter 5, we examine generalization of dynamics learning over time. Overall, our findings suggest that dynamics learning is a process involving local adaptation of the neural control signals for movement, and that interpolation between these instances of local learning is possible. These findings suggest that the apparent ease with which humans move in new situations may depend on interpolation between instances of previous learning that were acquired in a range of nearby situations. We elaborate on this idea in a General Discussion in Chapter 6.
La capacité de l'être humain à adapter ses mouvements à une grande variété de situations témoigne de la grande plasticité du contrôle neuronal du mouvement. Cette plasticité peut s'étudier via l'apprentissage dynamique qui correspond au processus par lequel le système nerveux met en place les signaux neuronaux nécessaires à la production des forces qui génèrent le mouvement. La présente thèse reporte une série d'expériences étudiant l'apprentissage dynamique et sa généralisation. Dans ces expériences, l'apprentissage dynamique est obtenu en amenant le sujet à compenser une perturbation mécanique systématiquement appliquée sur le bras par un robot au cours de l'exécution d'un mouvement d'atteinte de cible. Plus particulièrement, nos études sont centrées sur deux propriétés importantes de l'apprentissage dynamique: (1) le fait que l'apprentissage dynamique impliquerait des instances d'adaptation locales et (2) le fait que la généralisation de cet apprentissage se fonderait sur une interpolation entre ces instances d'adaptation locales.
Après une introduction générale dans le Chapitre 1, le Chapitre 2 examine si l'étendue de la généralisation de l'apprentissage dynamique dépend de l'étendue des situations rencontrées lors de la phase d'apprentissage. Le Chapitre 3 est consacré à l'étude de la généralisation de l'apprentissage dynamique à des mouvements de différentes amplitudes. Au Chapitre 4, nous montrons ensuite que la généralisation de l'apprentissage ne relève pas seulement de ces aspects méthodologiques. Comme le rapporte le Chapitre 2, la généralisation dépend aussi de l'étendue de l'apprentissage, elle-même liée aux propriétés biomécaniques des effecteurs. Le Chapitre 5 est consacré à l'évolution de la généralisation de l'apprentissage dynamique au cours du temps. L'ensemble de nos travaux suggère que l'apprentissage dynamique est un processus impliquant une adaptation locale des signaux du contrôle neuronal du mouvement et que l'interpolation de ces instances d'apprentissage local est possible. Ainsi, comme nous le discutons au Chapitre 6, l'apparente facilité de l'être humain à adapter ses mouvements à des situations nouvelles pourrait en réalité être le fruit de l'interpolation entre les instances d'apprentissages antérieurs acquis dans une gamme de situations similaires.

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39

Peirce, Heather Jean. "The dynamics of learning partnerships : case studies from Queensland." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16248/.

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This study examines the emerging notion of learning partnerships. As the study of such partnerships is a nascent research field, no single definition has yet emerged in the literature. However, within an uncertain and rapidly changing global context, two strategic initiatives have been identified which will support individuals, communities and organisations in their transition to a knowledge-based economy whilst building capacity for change and renewal. These two strategies are fostering learning communities/regions/towns and developing learning partnerships between multiple stakeholders. The term "learning partnership" has appeared in a wide variety of literatures including those of adult learning, management, social science and education. Working papers and emerging case reports identify a diversity of applications and a range of operational models or configurations that link multiple stakeholders. Learning partnerships have been associated with vocational education and training, innovation and research, lifelong learning, organisational learning and knowledge cultivation. These literatures reveal a paucity of Australian research to explain how multiple stakeholders form and develop these configurations, particularly in the Queensland context. The purpose of this study is to build deeper understanding of the meaning of a learning partnership in the Australian and (more precisely) the Queensland context. A working definition of a learning partnership, adopted as the basis for the research, indicates a strategy designed to foster continuous learning, collaboration, innovation and renewal in response to the demands of the knowledge-based economy and knowledge and learning societies. The research focuses on organisational arrangements in order for the researcher to gain deeper understanding from the key stakeholders in their work environments. Three diverse situations were selected for detailed exploration of their issues, relationships, activities, processes and working knowledge. With a view to contributing to emerging theory, an organisational case study methodology was adopted to identify and explore the nature of the relationships and issues confronting the key stakeholders in three Queensland-based learning partnerships. An interpretive theoretical framework draws on the social theory of symbolic interactionism and the "systems thinking" of General Systems Theory. An interpretivist perspective influenced the case study research strategy and guided data collection, analysis and reporting. Within the case studies, data collection methods included observations, informal meetings, synergetic focus groups, semi-structured interviews, diary notes, researcher memos and documents. From these multiple data sources, the researcher was able to assemble three case files. The inductive process for within-case analysis for the case reports, and later, cross-case analysis, integrated as a form of constant comparison technique, was used as a basis for presenting findings. These findings are reported as three separate "in progress" models to address three interrelated research questions. The case reports explain complex and interconnected organisational arrangements - evolving, adapting and responding to internal and external tensions. While there is considerable activity which could be regarded as representing learning partnerships, there is no cohesive policy framework to support such partnerships, and much ambiguity, "muddy" definitions and unclear terminology. It appears that a "new breed" of knowledge-worker is emerging - linking, networking, interacting, exchanging - to work across organisational intersections. The study shows that like "herding cats", co-ordinating and managing the inter relationships at the organisational intersection take time, resources, vision, processes for interaction, individual willingness and "in-kind" support. Whilst there is opportunity for linking disparate groups to cross-fertilise ideas, working knowledge, and information, and there is the potential to cultivate a knowledge and learning ecosystem (a fertile compost heap for knowledge generation and an innovative learning system) - "intellectual horsepower" - such configurations may also derail, realign or stagnate. It is individual stakeholders who form the relationships, interact, share ideas, and build networks, and it is the individual who maintains the relationships, engages in the process and learns from the experience. Therein lies a paradox between the strength of diversity of the collective (synergies) and their weakness as the relationships may be compromised by a single individual who withdraws or transfers. Drawing on a computing analogy, this could be akin to "corruption" in a system which may not be sufficiently robust to tolerate ambiguity, or a system that is too inflexible to survive threats while maintaining the momentum to adapt and renew. On the basis of this research it would appear that a more robust or resilient paradigm is emerging with interconnected, blurred boundaries and much "talking and thinking" about more sustainable futures. The study identifies these as indicative of wider social and economic changes. The thesis proposes three conceptual models as particularly useful in interpreting these "shifting systems and shifting paradigms": the concentric, the centripetal, and the plutonic.

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40

Choo, Kiam. "Learning hyperparameters for neural network models using Hamiltonian dynamics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0008/MQ53385.pdf.

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41

Cornish, Hannah. "Language adapts : exploring the cultural dynamics of iterated learning." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5603.

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Human languages are not just tools for transmitting cultural ideas, they are themselves culturally transmitted. This single observation has major implications for our understanding of how and why languages around the world are structured the way they are, and also for how scientists should be studying them. Accounting for the origins of what turns out to be such a uniquely human ability is, and should be, a priority for anyone interested in what makes us different from every other lifeform on Earth. The way the scientific community thinks about language has seen considerable changes over the years. In particular, we have witnessed movements away from a purely descriptive science of language, towards a more explanatory framework that is willing to embrace the difficult questions of not just how individual languages are currently structured and used, but also how and why they got to be that way in the first place. Seeing languages as historical entities is, of course, nothing new in linguistics. Seeing languages as complex adaptive systems, undergoing processes of evolution at multiple levels of interaction however, is. Broadly speaking, this thesis explores some of the implications that this perspective on language has, and argues that in addition to furthering our understanding of the processes of biological evolution and the mechanisms of individual learning required specifically for language, we also need to be mindful of the less well-understood cultural processes that mediate between the two. Human communication systems are not just direct expressions of our genes. Neither are they independently acquired by learners anew at every generation. Instead, languages are transmitted culturally from one generation to another, creating an opportunity for a different kind of evolutionary channel to exist. It is a central aim of this thesis to explore some of the adaptive dynamics that such a cultural channel has, and investigate the extent to which certain structural and statistical properties of language can be directly explained as adaptations to the transmission process and the learning biases of speakers. In order to address this aim, this thesis takes an experimental approach. Building on a rich set of empirical results from various computational simulations and mathematical models, it presents a novel methodological framework for exploring one type of cultural transmission mechanism, iterated learning, in the laboratory using human participants. In these experiments, we observe the evolution of artificial languages as they are acquired and then transmitted to new learners. Although there is no communication involved in these studies, and participants are unaware that their learning efforts are being propagated to future learners, we find that many functional features of language emerge naturally from the different constraints imposed upon them during transmission. These constraints can take a variety of forms, both internal and external to the learner. Taken collectively, the data presented here suggest several points: (i) that iterated language learning experiments can provide us with new insights about the emergence and evolution of language; (ii) that language-like structure can emerge as a result of cultural transmission alone; and (iii) that whilst structure in these systems has the appearance of design, and is in some sense ‘created’ by intentional beings, its emergence is in fact wholly the result of non-intentional processes. Put simply, cultural evolution plays a vital role in language. This work extends our framework for understanding it, and offers a new method for investigating it.

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42

Wong, Man Chiu. "Essays on learning dynamics, monetary policy and macroeconomic outcomes /." view abstract or download file of text, 2002. http://wwwlib.umi.com/cr/uoregon/fullcit?p3055723.

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Thesis (Ph. D.)--University of Oregon, 2002.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 161-169). Also available for download via the World Wide Web; free to University of Oregon users.

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43

Barfuss, Wolfram. "Learning dynamics and decision paradigms in social-ecological dilemmas." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20127.

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Kollektives Handeln ist erforderlich um nachhaltige Entwicklungspfade in gekoppelten sozial-ökologischen Systemen zu erschließen, fernab von gefährlichen Kippelementen. Ohne anderen Modellierungsprinzipien ihren Nutzen abzuerkennen, schlägt diese Dissertation die Agent-Umwelt Schnittstelle als die mathematische Grundlage für das Modellieren sozial-ökologischer Systeme vor. Zuerst erweitert diese Arbeit eine Methode aus der Literatur der statistischen Physik über Lerndynamiken, um einen deterministischen Grenzübergang von etablierten Verstärkungslernalgorithmen aus der Forschung zu künstlicher Intelligenz herzuleiten. Die resultierenden Lerndynamiken zeigen eine große Bandbreite verschiedener dynamischer Regime wie z.B. Fixpunkte, Grenzzyklen oder deterministisches Chaos. Zweitens werden die hergeleiteten Lerngleichungen auf eine neu eingeführte Umwelt, das Ökologisches Öffentliches Gut, angewendet,. Sie modelliert ein gekoppeltes sozial-ökologisches Dilemma und erweitert damit etablierte soziale Dilemmaspiele um ein ökologisches Kippelement. Bekannte theoretische und empirische Ergebnisse werden reproduziert und neuartige, qualitativ verschiedene Parameterregime aufgezeigt, darunter eines, in dem diese belohnungsoptimierenden Lern-Agenten es vorziehen, gemeinsam unter einem Kollaps der Umwelt zu leiden, als in einer florierenden Umwelt zu kooperieren. Drittens stellt diese Arbeit das Optimierungsparadigma der Lern-Agenten in Frage. Die drei Entscheidungsparadimen ökonomischen Optimierung, Nachhaltigkeit und Sicherheit werden systematisch miteinander verglichen, während sie auf das Management eines umweltlichen Kippelements angewendet werden. Es wird gezeigt, dass kein Paradigma garantiert, Anforderungen anderer Paradigmen zu erfüllen, sowie dass das Fehlen eines Meisterparadigmas von besonderer Bedeutung für das Klimasystem ist, da dieses sich am Rand zwischen Parameterbereichen befinden kann, wo ökonomische Optimierung weder nachhaltig noch sicher wird.
Collective action is required to enter sustainable development pathways in coupled social-ecological systems, safely away from dangerous tipping elements. Without denying the usefulness of other model design principles, this thesis proposes the agent-environment interface as the mathematical foundation for the design of social-ecological system models. First, this work refines techniques from the statistical physics literature on learning dynamics to derive a deterministic limit of established reinforcement learning algorithms from artificial intelligence research. Illustrations of the resulting learning dynamics reveal a wide range of different dynamical regimes, such as fixed points, periodic orbits and deterministic chaos. Second, the derived multi-state learning equations are applied to a newly introduced environment, the Ecological Public Good. It models a coupled social-ecological dilemma, extending established repeated social dilemma games by an ecological tipping element. Known theoretical and empirical results are reproduced and novel qualitatively different parameter regimes are discovered, including one in which these reward-optimizing agents prefer to collectively suffer in environmental collapse rather than cooperating in a prosperous environment. Third, this thesis challenges the reward optimizing paradigm of the learning equations. It presents a novel formal comparison of the three decision paradigms of economic optimization, sustainability and safety for the governance of an environmental tipping element. It is shown that no paradigm guarantees fulfilling requirements imposed by another paradigm. Further, the absence of a master paradigm is shown to be of special relevance for governing the climate system, since the latter may reside at the edge between parameter regimes where economic welfare optimization becomes neither sustainable nor safe.

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44

Hoffman, Daniel S. "The dynamics between leadership and learning in school principals /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488188894439549.

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45

Gibbs, Christopher. "Heterogeneous Expectations, Forecast Combination, and Economic Dynamics." Thesis, University of Oregon, 2013. http://hdl.handle.net/1794/13279.

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This dissertation examines the forecast model selection problem in economics in both theoretical and empirical settings. The forecast model selection problem is that there often exists a menu of different suitable models to forecast the same economic variable of interest. The theoretical portion of this dissertation considers agents who face this problem in two distinct scenarios. The first scenario considers the case where agents possess a menu of different forecast techniques which includes rational expectations but where the selection of rational expectations is costly. The assumptions that are necessary to include rational expectations as a choice are characterized and the equilibrium dynamics of a model under the appropriate assumptions is studied and shown to exhibit chaotic dynamics. The second scenario considers agents who possess a menu of econometric forecast models and examines the equilibrium outcomes when agents combine the different forecasts using strategies suggested by the forecasting literature. The equilibrium outcomes under these forecasting assumptions are shown to exhibit time-varying volatility and endogenous structural breaks, which are common features of macroeconomic data. The empirical portion of the dissertation proposes a new dynamic combination strategy for the forecast model selection problem to forecast inflation. The procedure builds on recent research on inflation persistence in the U.S. and on explanations for the efficacy of simple combination strategies, often referred to as the forecast combination puzzle. The new combination strategy is shown to forecast well in real-time out-of-sample forecasting exercises.

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46

Wang, Junlin. "Video based recognition of human dynamics : a machine learning approach." Thesis, University of Exeter, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439806.

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47

Nagel, Lynette. "The dynamics of learner participation in a virtual learning environment." Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-03032009-160447.

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48

Nagel, Lynette. "The dynamics of learner participation in a virtual learning environment." Thesis, University of Pretoria, 2008. http://hdl.handle.net/2263/22951.

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While online students should take charge of their own learning and form collaborative learning communities, constructivist instructors should scaffold online learning without dominating course discussions. This research continues the longitudinal investigation of web-based courses at the Faculty of Education, University of Pretoria. The mixed methodological approach this investigation followed consisted predominantly of qualitative methods, augmented with quantitative approaches. I used two distinct online tools to explore student participation in an eight-week online Masters’-level course delivered via the WebCT™ platform. First, I reviewed the use of metaphors in the literature by a framework of requirements for successful online learning. The use of metaphor supports constructivism, facilitates course interaction, helps to avoid students’ initial inertia in online discussions, and contributes to the development of virtual learning communities. I researched how an explanatory metaphor as tool supported online participation and indicated that metaphors eased students’ communication of important and difficult issues. Secondly, I used the tool of a covert virtual student that also acted as an additional facilitator and course helper. I examined the ethical implications of the carefully concealed real identity of the mythical online helper, methical Jane. As she took part in all course activities and assignments, as well as providing her co-students with cognitive and technical support, the students accepted and integrated her presence in their virtual learning community. I consequently analysed students’ reactions to her identity after disclosure of her origin after the course. Although the exposure precipitated students’ shock, disbelief and dismay as she was a convincing virtual student, they did not object to the presence of a virtual student, but rather felt betrayed due to her hidden real identity. The benefits of this teaching intervention include experts supplying technical expertise, multiple faculty enriching the learning experience, and support and teaching assistants and tutors participating with smaller groups in large online classes. I further examined how frequency of course access, discussion postings, collaborative behaviour and integration into a virtual learning community relate to learning and course completion. Quantitative indices indicated highly significant differences between the stratifications of student performance. Absent and seldom-contributing students risked missing the benefits of the online learning community. Students were discontent with peers who rarely and insufficiently contributed to group assignments. Low participation varied from only reading, skimming, or deliberately harvesting others’ contributions, to high student contributions of little value. Conclusions on the formation of an online learning community indicate that the passport to membership of the community is quality participation, rather than prior peer acquaintance. I indicated that students’ learning benefited from contributing high quality inputs to online learning communities while students with poor participation did not benefit from the online learning community. Online facilitators contribute to students’ learning through the timeliness and quality of tailored scaffolding. Recommendations for future research include uncovering the reasons for students’ stressful experiences of online learning; the effect of online assessment on student course participation; the alignment of learning metaphors in multi-cultural learning environments; and the support of non-participating online students.
Thesis (PHD)--University of Pretoria, 2009.
Curriculum Studies
unrestricted

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49

Coen, Michael Harlan. "Multimodal dynamics : self-supervised learning in perceptual and motor systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34022.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 178-192).
This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the extraordinary degree of interaction between sensory modalities during the course of ordinary perception. We develop a framework for creating artificial perceptual systems that draws on these findings, where the primary architectural motif is the cross-modal transmission of perceptual information to enhance each sensory channel individually. We present self-supervised algorithms for learning perceptual grounding, intersensory influence, and sensorymotor coordination, which derive training signals from internal cross-modal correlations rather than from external supervision. Our goal is to create systems that develop by interacting with the world around them, inspired by development in animals. We demonstrate this framework with: (1) a system that learns the number and structure of vowels in American English by simultaneously watching and listening to someone speak. The system then cross-modally clusters the correlated auditory and visual data.
(cont.) It has no advance linguistic knowledge and receives no information outside of its sensory channels. This work is the first unsupervised acquisition of phonetic structure of which we are aware, outside of that done by human infants. (2) a system that learns to sing like a zebra finch, following the developmental stages of a juvenile zebra finch. It first learns the song of an adult male and then listens to its own initially nascent attempts at mimicry through an articulatory synthesizer. In acquiring the birdsong to which it was initially exposed, this system demonstrates self-supervised sensorimotor learning. It also demonstrates afferent and efferent equivalence - the system learns motor maps with the same computational framework used for learning sensory maps.
by Michael Harlan Coen.
Ph.D.

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50

Nair, Ashwati. "Capturing Vortex Dynamics to Predict Acoustic Response using Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546427424013197.

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