Relevant bibliographies by topics / Autonomous learning / Dissertations / Theses
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Dissertations / Theses on the topic 'Autonomous learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Ivanov, Yuri A. 1967. "State discovery for autonomous learning." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8324.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.<br>Includes bibliographical references (p. 163-171).<br>This thesis is devoted to the study of algorithms for early perceptual learning for an autonomous agent in the presence of feedback. In the framework of associative perceptual learning with indirect supervision, three learning techniques are examined in detail: * short-term on-line memory-based model learning; * long-term on-line distribution-based statistical estimation; * mixed on- and off-line continuou
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2

Lai, Fei Long. "Autonomy and teacher learning : developing autonomous teacher learning." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409600.

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3

Marvel, Jeremy Alan. "Autonomous Learning for Robotic Assembly Applications." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1268187684.

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Thesis (Doctor of Philosophy)--Case Western Reserve University, 2010<br>Department of EECS - Computer Engineering Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
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4

Gómez, Betancur Gabriel J. "Adaptive learning mechanisms for autonomous robots /." Zürich, 2007. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000253587.

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5

Strömgren, Oliver. "Deep Learning for Autonomous Collision Avoidance." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-147693.

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Deep learning has been rapidly growing in recent years obtaining excellent results for many computer vision applications, such as image classification and object detection. One aspect for the increased popularity of deep learning is that it mitigates the need for hand-crafted features. This thesis work investigates deep learning as a methodology to solve the problem of autonomous collision avoidance for a small robotic car. To accomplish this, transfer learning is used with the VGG16 deep network pre-trained on ImageNet dataset. A dataset has been collected and then used to fine-tune and valid
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6

Tambouratzis, George D. "Autonomous learning in logic neural networks." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334608.

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7

Beale, Dan. "Autonomous visual learning for robotic systems." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

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This thesis investigates the problem of visual learning using a robotic platform. Given a set of objects the robots task is to autonomously manipulate, observe, and learn. This allows the robot to recognise objects in a novel scene and pose, or separate them into distinct visual categories. The main focus of the work is in autonomously acquiring object models using robotic manipulation. Autonomous learning is important for robotic systems. In the context of vision, it allows a robot to adapt to new and uncertain environments, updating its internal model of the world. It also reduces the amount
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Zeltner, Felix. "Autonomous Terrain Classification Through Unsupervised Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893.

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A key component of autonomous outdoor navigation in unstructured environments is the classification of terrain. Recent development in the area of machine learning show promising results in the task of scene segmentation but are limited by the labels used during their supervised training. In this work, we present and evaluate a flexible strategy for terrain classification based on three components: A deep convolutional neural network trained on colour, depth and infrared data which provides feature vectors for image segmentation, a set of exchangeable segmentation engines that operate in this f
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9

Roberts, Gary Allen. "Classifier systems for situated autonomous learning." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/20146.

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The ability to learn from experience is a key aspect of intelligence. Incorporating this ability into a computer is a formidable problem. Genetic algorithms coupled to learning classifier systems are powerful tools for tackling this task. While genetic algorithms can be shown to be near optimal solutions for the search task they perform, no similar proof exists for classifier systems. My research investigated two aspects of classifier systems, classifier selection and credit assignment. Explicit world models, look ahead and incremental planning are incorporated into the classifier system frame
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10

Leung, W. H. "Does project-based learning change learners' attitudes towards autonomous learning?" Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31650491.

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11

Leung, W. H., and 梁惠卿. "Does project-based learning change learners' attitudes towards autonomous learning?" Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31650491.

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12

Scholz, Jonathan. "Physics-based reinforcement learning for autonomous manipulation." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54366.

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With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically in the past decade, with applications ranging from house cleaning to food service to health care. To date, the majority of the planning and control machinery for these systems are carefully designed by human engineers. A large portion of this effort goes into selecting the appropriate models and control techniques for each application, and these skills take years to master. Relieving the burden on human experts is therefo
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13

Cooker, Lucy. "Formative (self-)assessment as autonomous language learning." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/13665/.

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While learner autonomy is often lauded as an important goal in language education, applied linguists have debated if it is a construct that has been given proper attention in terms of definition and assessment. In order to address this debate the researcher implemented a two-phase study within the context of higher education. Theories of learner autonomy, sustainable assessment and transformative learning guided the study design. In the research design, the nexus between language learner autonomy and assessment as learning was first explored in phase one of the study. Here survey methodology w
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14

Colombini, Esther Luna. "Module-based learning in autonomous mobile robots." Instituto Tecnológico de Aeronáutica, 2005. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=213.

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A informação disponível para robôs em tarefas reais encontra-se amplamente distribuída tanto no espaço quanto no tempo, fazendo com que o agente busque informações relevantes. Neste trabalho, uma solução que usa conhecimento qualitativo e quantitativo da tarefa é implementada a fim de permitir que tarefas robóticas reais sejam tratáveis por algoritmos de Aprendizagem por Reforço (AR). Os passos deste procedimento incluem: 1) decompor a tarefa completa em tarefas menores, usando abstração e macro-operadores, para que um espaço de ações discreto seja atingido; 2) aplicar um modelo de representaç
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Bengtsson, Ivar. "Autonomous Overtaking with Learning Model Predictive Control." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.

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We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built f
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Jennings, Alan Lance. "Autonomous Motion Learning for Near Optimal Control." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1344016631.

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17

Glaser, Rollin. "Facilitating adult learning in semi-autonomous work groups /." Access Digital Full Text version, 1992. http://pocketknowledge.tc.columbia.edu/home.php/bybib/1106268x.

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Thesis (Ed.D.)--Teachers Colloge, Columbia University, 1992.<br>Includes tables. Typescript; issued also on microfilm. Sponsor: Jack Mezirow. Dissertation Committee: Elizabeth Kassl. Includes bibliographical references (leaves 338-355).
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Street, Vanessa. "Autonomous language learning : the role of the computer." Thesis, University of Kent, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342272.

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19

Chen, Zhilu. "Computer Vision and Machine Learning for Autonomous Vehicles." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/488.

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"Autonomous vehicle is an engineering technology that can improve transportation safety, alleviate traffic congestion and reduce carbon emissions. Research on autonomous vehicles can be categorized by functionality, for example, object detection or recognition, path planning, navigation, lane keeping, speed control and driver status monitoring. The research topics can also be categorized by the equipment or techniques used, for example, image processing, computer vision, machine learning, and localization. This dissertation primarily reports on computer vision and machine learning algorithms a
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20

Richter, Charles Andrew. "Autonomous navigation in unknown environments using machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112457.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 165-175).<br>In this thesis, we explore the problem of high-speed autonomous navigation for a dynamic mobile robot in unknown environments. Our objective is to navigate from start to goal in minimum time, given no prior knowledge of the map, nor any explicit knowledge of the environment distribution. Faced with this challenge, most practical receding-horizon navigation methods simply restrict their action
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Amini, Alexander Andre. "Robust end-to-end learning for autonomous vehicles." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118031.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-64).<br>Deep learning has been successfully applied to "end-to-end" learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. While these works support reactionary control, the representation learned is not usable for higher-level decision making required for autonomous navigation. This thesis tackles the proble
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22

Mendes, Caio César Teodoro. "Navigability estimation for autonomous vehicles using machine learning." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25092017-102021/.

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Autonomous navigation in outdoor, unstructured environments is one of the major challenges presents in the robotics field. One of its applications, intelligent autonomous vehicles, has the potential to decrease the number of accidents on roads and highways, increase the efficiency of traffic on major cities and contribute to the mobility of the disabled and elderly. For a robot/vehicle to safely navigate, accurate detection of navigable areas is essential. In this work, we address the task of visual road detection where, given an image, the objective is to classify its pixels into road or non-
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23

Dsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.

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24

Ticktin, Jordan M. "Resource-Constrained Project Scheduling with Autonomous Learning Effects." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2103.

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It's commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. This thesis considers the idea that learning effects could allow selected activities to be completed within reduced time, if they're scheduled after activities where workers learn relevant skills. This paper computationally explores the effect of this autonomous, intra-project learning on optimal makespan and problem difficulty. A learning extension is proposed to the standard RCPSP scheduling problem. Multiple parameters are considered, including project s
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Shao, Yunming. "Image-based Perceptual Learning Algorithm for Autonomous Driving." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503302777088283.

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26

Pradhan, Neil. "Deep Reinforcement Learning for Autonomous Highway Driving Scenario." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289444.

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We present an autonomous driving agent on a simulated highway driving scenario with vehicles such as cars and trucks moving with stochastically variable velocity profiles. The focus of the simulated environment is to test tactical decision making in highway driving scenarios. When an agent (vehicle) maintains an optimal range of velocity it is beneficial both in terms of energy efficiency and greener environment. In order to maintain an optimal range of velocity, in this thesis work I proposed two novel reward structures: (a) gaussian reward structure and (b) exponential rise and fall reward s
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Al-Khoury, Fadi. "Safety of Machine Learning Systems in Autonomous Driving." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218020.

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Machine Learning, and in particular Deep Learning, are extremely capable tools for solving problems which are difficult, or intractable to tackle analytically. Application areas include pattern recognition, computer vision, speech and natural language processing. With the automotive industry aiming for increasing amount of automation in driving, the problems to solve become increasingly complex, which appeals to the use of supervised learning methods from Machine Learning and Deep Learning. With this approach, solutions to the problems are learned implicitly from training data, and inspecting
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NGUYEN, QUANG ANH. "Scene Understanding for Autonomous Manipulation with Deep Learning." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/939929.

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Over the past few years, deep learning techniques have achieved tremendous success in many visual understanding tasks such as object detection, image segmentation, and caption generation. Despite this thriving in computer vision and natural language processing, deep learning has not yet shown signicant impact in robotics. Due to the gap between theory and application, there are many challenges when applying the results of deep learning to the real robotic systems. In this study, our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods tha
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29

Kebbati, Mohammed-Yassine. "Learning-based reinforced control strategies for autonomous vehicles." Electronic Thesis or Diss., université Paris-Saclay, 2023. https://www.biblio.univ-evry.fr/theses/2023/interne/2023UPAST071.pdf.

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Dans un monde où les gens sont constamment en mouvement et ou la mobilité personnelle est préférée, le nombre de voitures personnelles sur la route ne cesse d'augmenter. Cela a induit plus de risque d'accidents de route, des conditions de circulation et de pollution de l'air plus dégradées. Par conséquent, les chercheurs travaillent depuis des décennies pour une transition vers la conduite autonome. Cette dernière a la capacité de remodeler la mobilité en diminuant les accidents de route, les embouteillages et la pollution de l'air, ce qui se traduirait par plus d'efficacité énergétique et de
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Lecerf, Ugo. "Robust learning for autonomous agents in stochastic environments." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS253.

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Nous explorons une approche par l'apprentissage par renforcement (RL) pour qu'un agent autonome soit robuste aux risques et incertitudes rencontrés dans une tâche de navigation. Nous étudions les effets des changements soudains des conditions de leur environnement sur des agents autonomes et explorons des méthodes qui permettent à un agent de correctement réagir aux imprévus de son environnement pour lesquelles il n'a pas été explicitement formé. Inspiré par le circuit de dopamine humain, la performance d'un agent RL est mesurée et optimisée en termes de récompenses et de pénalités qu'il reçoi
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Messaoud, Kaouther. "Deep learning based trajectory prediction for autonomous vehicles." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS048.

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La prédiction de trajectoire des agents avoisinants d'un véhicule autonome est essentielle pour la conduite autonome afin d'effectuer une planification de trajectoire d'une manière efficace. Dans cette thèse, nous abordons la problématique de prédiction de trajectoire d'un véhicule cible dans deux environnements différents ; une autoroute et une zone urbaine (intersection, rond-point, etc.). Dans ce but, nous développons des solutions basées sur l'apprentissage automatique profond en mettant en phase les interactions entre le véhicule cibles et les éléments statiques et dynamiques de la scène.
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Chung, Jen Jen. "Learning to soar: exploration strategies in reinforcement learning for resource-constrained missions." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11733.

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An unpowered aerial glider learning to soar in a wind field presents a new manifestation of the exploration-exploitation trade-off. This thesis proposes a directed, adaptive and nonmyopic exploration strategy in a temporal difference reinforcement learning framework for tackling the resource-constrained exploration-exploitation task of this autonomous soaring problem. The complete learning algorithm is developed in a SARSA() framework, which uses a Gaussian process with a squared exponential covariance function to approximate the value function. The three key contributions of this thesis for
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Santamaria, Juan Carlos. "Learning adaptive reactive agents." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/9247.

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34

Zhang, Yunfei. "Robot learning for autonomous navigation in a dynamic environment." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54912.

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This dissertation addresses autonomous navigation of robots in a dynamic environment where the existence of moving and/or unknown objects leads to more serious challenges for the robots than those when operating in a traditional stationary environment. Therefore, the use of learning capabilities to facilitate proper robotic operation in a dynamic environment has become an important research area in the past decade. This dissertation proposes several novel learning-based methods to overcome the shortcomings in the existing approaches of autonomous navigation. Three aspects are addressed in the
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Campbell, Tanner, and Tanner Campbell. "A Deep Learning Approach to Autonomous Relative Terrain Navigation." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/626706.

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Autonomous relative terrain navigation is a problem at the forefront of many space missions involving close proximity operations to any target body. With no definitive answer, there are many techniques to help cope with this issue using both passive and active sensors, but almost all require high fidelity models of the associated dynamics in the environment. Convolutional Neural Networks (CNNs) trained with images rendered from a digital terrain map (DTM) of the body’s surface can provide a way to side-step the issue of unknown or complex dynamics while still providing reliable autonomous navi
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Kerin, Michael A. "Self-organisation and autonomous learning in logical neural networks." Thesis, Brunel University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303172.

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37

Hill, Jerry L., and Randall P. Mora. "An Autonomous Machine Learning Approach for Global Terrorist Recognition." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581675.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>A major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent pers
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Öfjäll, Kristoffer. "Adaptive Supervision Online Learning for Vision Based Autonomous Systems." Doctoral thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125916.

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Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world.   Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or we
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Słowiński, Witold. "Autonomous learning of domain models from probability distribution clusters." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=211059.

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Nontrivial domains can be difficult to understand and the task of encoding a model of such a domain can be difficult for a human expert, which is one of the fundamental problems of knowledge acquisition. Model learning provides a way to address this problem by allowing a predictive model of the domain's dynamics to be learnt algorithmically, without human supervision. Such models can provide insight about the domain to a human or aid in automated planning or reinforcement learning. This dissertation addresses the problem of how to learn a model of a continuous, dynamic domain, from sensory obs
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Hagström, Jesper. "Conditional Imitation Learning for Autonomous Driving : Comparing two approaches." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292200.

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Syftet med denna studie var att bygga, träna och testa two olika självkörande agenter med hjälp av maskininlärningstekniker, specifikt neurala nätverk. För att träna agenterna användes en teknik kallad Imitationsinlärning. Imitationsinlärning är en teknik för att lära agenter sekventiell beslutsfattning genom demostrering från en expert (vanligtvis en människa). Två något olika nätverksarkitekturer jämfördes. Skillnaden mellan dessa var att den kontrollmodul för en specifik intention (vilket här betecknar t.ex. åt vilket håll man ska köra i en korsning) var placerad antingen tidigt eller sent
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Warren, Emily Amanda. "Machine Learning for Road Following by Autonomous Mobile Robots." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1222357062.

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Li, Jiachen Li. "The Simulation of Autonomous Racing Based on Reinforcement Learning." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523566974378793.

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Karakurt, Murat Özdemir Serhan. "Data driven modeling using reinforcement learning in autonomous agents/." [s.l.]: [s.n.], 2003. http://library.iyte.edu.tr/tezler/master/makinamuh/T000276.pdf.

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44

Magnusson, Filip. "Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450.

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With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks to make a robot car avoid obstacles. The robot car has a monocular camera, and our approach is to use the images taken by the camera as input, and then output a steering command. Using this method the car is to avoid any object in front of it. In order to lower the amount of training data we use models that are pretrained on ImageNet, a large image database containing millions of images. The model are then trained on our own dataset, which contains o
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Kim, Backyoung. "Autonomous elementary English learning in Korea using mediated structures." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2449.

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46

Kim, Richard S. M. Massachusetts Institute of Technology. "A computational model of moral learning for autonomous vehicles." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122897.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 75-81).<br>We face a future of delegating many important decision making tasks to artificial intelligence (AI) systems as we anticipate widespread adoption of autonomous systems such as autonomous vehicles (AV). However, recent string of fatal accidents involving AV reminds us that delegating certain decisions making tasks have deep ethical complications. As a result, building et
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Ouaknine, Arthur. "Deep learning for radar data exploitation of autonomous vehicle." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT007.

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La conduite autonome exige une compréhension détaillée de scènes de conduite complexes. La redondance et la complémentarité des capteurs du véhicule permettent une compréhension précise et robuste de l'environnement, augmentant ainsi le niveau de performance et de sécurité. Cette thèse se concentre sur le RADAR automobile, qui est un capteur actif à faible coût mesurant les propriétés des objets environnants, y compris leur vitesse relative, et qui a l'avantage de ne pas être affecté par des conditions météorologiques défavorables.Avec les progrès rapides de l'apprentissage profond et la dispo
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Smart, Royce Raymond, and roycesmart@hotmail com. "Evolutionary Control of Autonomous Underwater Vehicles." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090331.143104.

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The goal of Evolutionary Robotics (ER) is the development of automatic processes for the synthesis of robot control systems using evolutionary computation. The idea that it may be possible to synthesise robotic control systems using an automatic design process is appealing. However, ER is considerably more challenging and less automatic than its advocates would suggest. ER applies methods from the field of neuroevolution to evolve robot control systems. Neuroevolution is a machine learning algorithm that applies evolutionary computation to the design of Artificial Neural Networks (ANN). T
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49

Bednall, Timothy Colin Psychology Faculty of Science UNSW. "Effects of self-regulatory aids on autonomous study." Publisher:University of New South Wales. Psychology, 2009. http://handle.unsw.edu.au/1959.4/43360.

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The experiments described in this thesis tested whether encouraging the use of self-regulatory learning strategies enhances the effectiveness of autonomous study for novices in a learning domain. Previous research and theory have suggested that high-achieving students are proficient at self-regulating their learning, and they do so using a range of learning strategies for planning their study, monitoring the effectiveness of their efforts and elaborating their knowledge. Information processing theories of instructional design suggest that learning is optimal when working memory load is managed
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50

Schmiterlöw, Maria. "Autonomous Path Following Using Convolutional Networks." Thesis, Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78670.

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Autonomous vehicles have many application possibilities within many different fields like rescue missions, exploring foreign environments or unmanned vehicles etc. For such system to navigate in a safe manner, high requirements of reliability and security must be fulfilled. This master's thesis explores the possibility to use the machine learning algorithm convolutional network on a robotic platform for autonomous path following. The only input to predict the steering signal is a monochromatic image taken by a camera mounted on the robotic car pointing in the steering direction. The convolutio
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