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Relevant bibliographies by topics / Modular Learning

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Academic literature on the topic 'Modular Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 June 2025

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Journal articles on the topic "Modular Learning"

1

Miall, Chris. "Modular motor learning." Trends in Cognitive Sciences 6, no. 1 (2002): 1–3. http://dx.doi.org/10.1016/s1364-6613(00)01822-2.

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Simpkins, Christopher, and Charles Isbell. "Composable Modular Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4975–82. http://dx.doi.org/10.1609/aaai.v33i01.33014975.

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Modular reinforcement learning (MRL) decomposes a monolithic multiple-goal problem into modules that solve a portion of the original problem. The modules’ action preferences are arbitrated to determine the action taken by the agent. Truly modular reinforcement learning would support not only decomposition into modules, but composability of separately written modules in new modular reinforcement learning agents. However, the performance of MRL agents that arbitrate module preferences using additive reward schemes degrades when the modules have incomparable reward scales. This performance degradation means that separately written modules cannot be composed in new modular reinforcement learning agents as-is – they may need to be modified to align their reward scales. We solve this problem with a Q-learningbased command arbitration algorithm and demonstrate that it does not exhibit the same performance degradation as existing approaches to MRL, thereby supporting composability.

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Frenger, Paul. "Learning forth with modular forth." ACM SIGPLAN Notices 35, no. 3 (2000): 25–30. http://dx.doi.org/10.1145/351159.351164.

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Ghahramani, Zoubin, and Daniel M. Wolpert. "Modular decomposition in visuomotor learning." Nature 386, no. 6623 (1997): 392–95. http://dx.doi.org/10.1038/386392a0.

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Ishikawa, Masumi. "Learning of modular structured networks." Artificial Intelligence 75, no. 1 (1995): 51–62. http://dx.doi.org/10.1016/0004-3702(94)00061-5.

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Isaila, Narcisa. "Learning systems with modular resources reused." Procedia - Social and Behavioral Sciences 15 (2011): 311–15. http://dx.doi.org/10.1016/j.sbspro.2011.03.092.

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Blackmon, James, David Byrd, Robert Cummins, Pierre Poirier, and Martin Roth. "Atomistic learning in non-modular systems." Philosophical Psychology 18, no. 3 (2005): 313–25. http://dx.doi.org/10.1080/09515080500177283.

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Sanchez, Ron, and Robert P. Collins. "Competing—and Learning—in Modular Markets." Long Range Planning 34, no. 6 (2001): 645–67. http://dx.doi.org/10.1016/s0024-6301(01)00099-1.

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Gunter, Karen South, and Sandra Matteson. "A Competency-Based Modular Learning Program." Home Healthcare Nurse: The Journal for the Home Care and Hospice Professional 20, no. 1 (2002): 51–55. http://dx.doi.org/10.1097/00004045-200201000-00013.

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Suzuki, Satoshi, and Naonori Ueda. "Adaptive clustering using modular learning architecture." Systems and Computers in Japan 34, no. 2 (2003): 70–80. http://dx.doi.org/10.1002/scj.1191.

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Dissertations / Theses on the topic "Modular Learning"

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Kumar, Shailesh. "Modular learning through output space decomposition /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004308.

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Hanna, Christopher J. "Hybrid modular reinforcement learning for game agent control." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553862.

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Intelligent virtual characters play an important role in creating an engaging player experience in modern computer games. Imbuing these characters with learning capabilities curtails the need to define every nuance of their behaviour during development. For this reason, there is growing interest in introducing machine learning techniques into game agent control architectures. However, computer game environments tend to be highly complex and dynamic, which necessitates the use of large state spaces to define effective agent behaviour. Traditional learning strategies are not suited to operating under these circumstances due to the "Curse of Dimensionality". Therefore, in order for their learning to be effective, agents require architectures that can handle complex dynamic state spaces. Within this thesis it is demonstrated that modular reinforcement learning and reactive / deliberative hybridisation techniques present a powerful combination for the implementation of effective game agent architectures. A novel approach to modular reinforcement learning is presented that utilises a fine granularity of modules. This new approach necessitated the development and evaluation of new methods for action selection, reward distribution and exploration. Furthermore, a new method of hybridising modular architectures with deliberative mechanisms is proposed and evaluated. Results demonstrate that agents implemented with hybrid reactive / deliberative architecture can outperform purely reactive and purely deliberative agents, particularly in resource constrained applications.

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Varshavskaya, Paulina. "Distributed reinforcement learning for self-reconfiguring modular robots." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42058.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 101-106).<br>In this thesis, we study distributed reinforcement learning in the context of automating the design of decentralized control for groups of cooperating, coupled robots. Specifically, we develop a framework and algorithms for automatically generating distributed controllers for self-reconfiguring modular robots using reinforcement learning. The promise of self-reconfiguring modular robots is that of robustness, adaptability and versatility. Yet most state-of-the-art distributed controllers are laboriously handcrafted and task-specific, due to the inherent complexities of distributed, local-only control. In this thesis, we propose and develop a framework for using reinforcement learning for automatic generation of such controllers. The approach is profitable because reinforcement learning methods search for good behaviors during the lifetime of the learning agent, and are therefore applicable to online adaptation as well as automatic controller design. However, we must overcome the challenges due to the fundamental partial observability inherent in a distributed system such as a self reconfiguring modular robot. We use a family of policy search methods that we adapt to our distributed problem. The outcome of a local search is always influenced by the search space dimensionality, its starting point, and the amount and quality of available exploration through experience.<br>(cont) We undertake a systematic study of the effects that certain robot and task parameters, such as the number of modules, presence of exploration constraints, availability of nearest-neighbor communications, and partial behavioral knowledge from previous experience, have on the speed and reliability of learning through policy search in self-reconfiguring modular robots. In the process, we develop novel algorithmic variations and compact search space representations for learning in our domain, which we test experimentally on a number of tasks. This thesis is an empirical study of reinforcement learning in a simulated lattice based self-reconfiguring modular robot domain. However, our results contribute to the broader understanding of automatic generation of group control and design of distributed reinforcement learning algorithms.<br>by Paulina Varshavskaya.<br>Ph.D.

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Frame, Charles Ian. "Supporting a non-modular professional doctorate." Thesis, Anglia Ruskin University, 2013. http://arro.anglia.ac.uk/311627/.

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Purpose: This research considers the design and operation of a non-modular professional doctorate to meet the needs of professionals working in the built environment who wish to obtain a doctoral qualification. It seeks to identify the essential components and support mechanisms to provide an alternative to other forms of doctorate which draws on their strengths while addressing some of their shortcomings. It answers questions regarding the suitability of a programme which can successfully operate within a reasonable timeframe. Research Design: The research is set in a real-life phenomenological paradigm concerning the experience and development of candidates registered for a professional doctorate. The conceptual framework governed both the design of the research and the design of a two-stage curriculum. Regular intervention and evaluation using action research methodology was used to improve practice. The research produced findings through multiple sources of evidence. Data were collected from course documentation, online discussion forums, focus groups, individual reflections and interviews. Findings: The work found that a community of practice consisting of candidates and staff, specifically focused on learning and the continuous development of candidates, provides a suitable vehicle for professional doctorate work. Candidates benefit from engaging in carefully constructed summative and formative assessment with prompt feedback. The assessment informed regular workshops containing an active learning format supplemented through additional support from a virtual learning environment. Crucially, all three components are required to support each other by drawing on their individual strengths. Conclusion: This action research project made a modest but significant contribution to curriculum development at doctoral level. The research developed a model which enabled academic practice to help candidates improve their professional practice. Self-motivated candidates with appropriate supervisory support can complete a professional doctorate within a realistic timeframe when there is carefully constructed synergy between their doctorate, its supporting mechanisms and their own professional practice.

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Bale, Tracey Ann. "Modular connectionist architectures and the learning of quantification skills." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/842886/.

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Modular connectionist systems comprise autonomous, communicating modules, achieving a behaviour more complex than that of a single neural network. The component modules, possibly of different topologies, may operate under various learning algorithms. Some modular connectionist systems are constrained at the representational level, in that the connectivity of the modules is hard-wired by the modeller; others are constrained at an architectural level, in that the modeller explicitly allocates each module to a specific subtask. Our approach aims to minimise these constraints, thus reducing the bias possibly introduced by the modeller. This is achieved, in the first case, through the introduction of adaptive connection weights and, in the second, by the automatic allocation of modules to subtasks as part of the learning process. The efficacy of a minimally constrained system, with respect to representation and architecture, is demonstrated by a simulation of numerical development amongst children. The modular connectionist system MASCOT (Modular Architecture for Subitising and Counting Over Time) is a dual-routed model simulating the quantification abilities of subitising and counting. A gating network learns to integrate the outputs of the two routes in determining the final output of the system. MASCOT simulates subitising through a numerosity detection system comprising modules with adaptive weights that self-organise over time. The effectiveness of MASCOT is demonstrated in that the distance effect and Fechner's law for numbers are seen to be consequences of this learning process. The automatic allocation of modules to subtasks is illustrated in a simulation of learning to count. Introducing feedback into one of two competing expert networks enables a mixture-of-experts model to perform decomposition of a task into static and temporal subtasks, and to allocate appropriate expert networks to those subtasks. MASCOT successfully performs decomposition of the counting task with a two-gated mixture-of-experts model and exhibits childlike counting errors.

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Karaoguz, Cem [Verfasser]. "Learning of information gathering in modular intelligent systems / Cem Karaoguz." Bielefeld : Universitätsbibliothek Bielefeld, 2012. http://d-nb.info/1036111636/34.

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Jacob, David. "Efficient reinforcement learning in agents through embodiment-based modular decomposition." Thesis, University of Hertfordshire, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427548.

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Tham, Chen Khong. "Modular on-line function approximation for scaling up reinforcement learning." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309702.

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Waknine, Jessica. "A Case Study of Student Cognitive Responses to Learning with Computer-Assisted Modular Curriculum." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/43864.

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Little is known about how students learn when using computer-assisted modular curriculum, if such curriculum truly promotes self-regulated learning, or if the cognitive principles of teaching and learning are integrated throughout the design of the modules. The purpose of this study was to investigate the phenomenon of student cognitive responses to learning with computer-assisted modular curriculum, based on the Phases and Subprocesses of Self-Regulation. This triangulation mixed methods case study connected qualitative and quantitative data derived from curriculum content analysis, student course evaluations, participant observations, and interviews. Thirty-six middle school students enrolled in an agricultural education course designed with computer-assisted modules served as the case study group. Data were transcribed, coded, and analyzed, leading to the emergence of six common themes. Overall, the design and content of the computer-assisted modules lack integral principles of teaching and learning. Participants prefer a mix of traditional and computer-assisted instruction because of the variety of instruction, opportunities for social learning, and the hands-on activities. When integrated properly, computer-assisted modules do not inhibit interactions among the teacher and the students. The activities associated with the modules do not encourage self-regulatory processes. However, self-regulation is innate and students engage in self-regulation at different levels during the learning experience. Despite intrinsic interest or value for a particular topic, participants felt it was always important to pay attention in school. Thus, when learning with computer-assisted modules, students engage in social learning with their peers and desire hands-on learning experiences, with or without the modules.<br>Master of Science in Life Sciences

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Gane, Brian D. "Can modular examples and contextual interference improve transfer?" Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11451.

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Two instructional design features hypothesized to affect problem solving performance, problem format and contextual interference, were investigated. Problem format was manipulated by altering the format of worked examples to demonstrate a molar or modular solution. Contextual interference was manipulated by randomizing the order in which problem categories were studied. Participants studied worked examples from 5 complex probability categories and solved 11 novel problems. The modular problem format reduced study time and the workload during study and increased performance on the subsequent test. Greater contextual interference increased study time but had no effect on workload or test performance. Additionally, a regression analysis demonstrated that mental workload partially mediated the effect of problem format on test performance. A separate regression analysis did not demonstrate that working memory capacity moderated the effect of problem format on mental workload.

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Books on the topic "Modular Learning"

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Murre, Jacob M. J. Learning and categorization in modular neural networks. Harvester Wheatsheaf, 1992.

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Learning and categorization in modular neural networks. L. Erlbaum Associates, 1992.

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Freedman, Harvey C. Learning Sage 50 accounting, 2014: A modular approach. Nelson Education, 2015.

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Flynn, Alun Peter. Modular learning product for module 8: Research and enquiry. University of East London, 1999.

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How to design effective text-based open learning: A modular course. McGraw-Hill, 1991.

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Modular learning in neural networks: A modularized approach to neural network classification. Wiley, 1992.

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Practical instructional design for open learning materials: A modular course covering open learning, computer-based training, multimedia. McGraw-Hill Book Co., 1994.

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Northedge, Andy. Teaching access: A tutor's handbook for the modular, open-learning access course Living in a changing society. Open University, 1992.

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O, Mitterer John, ed. Psychology: Modules for active learning. Wadsworth, Engage Learning, 2012.

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O, Mitterer John, ed. Psychology: Modules for active learning. Thomson/Wadsworth, 2009.

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Book chapters on the topic "Modular Learning"

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Reeve, Henry W. J., Tingting Mu, and Gavin Brown. "Modular Dimensionality Reduction." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10925-7_37.

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Shin, Hyunjung, Hyoungjoo Lee, and Sungzoon Cho. "Observational Learning with Modular Networks." In Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44491-2_19.

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Mitchell, David, and Eugenia Ternovska. "Clause-Learning for Modular Systems." In Logic Programming and Nonmonotonic Reasoning. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23264-5_37.

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Ishikawa, Masumi, and Kosuke Ueno. "Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_57.

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Maffei, Giovanni, Jordi-Ysard Puigbò, and Paul F. M. J. Verschure. "Learning Modular Sequences in the Striatum." In Biomimetic and Biohybrid Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63537-8_52.

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Liu, Zhibin, Xiaoqin Zeng, and Huiyi Liu. "A Modular Hierarchical Reinforcement Learning Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31576-3_48.

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Christensen, David Johan, Mirko Bordignon, Ulrik Pagh Schultz, Danish Shaikh, and Kasper Stoy. "Morphology Independent Learning in Modular Robots." In Distributed Autonomous Robotic Systems 8. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00644-9_34.

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Kalmár, Zsolt, Csaba Szepesvári, and András Lorincz. "Modular Reinforcement Learning: An Application to a Real Robot Task." In Learning Robots. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49240-2_3.

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Neufeld, Emery, Ezio Bartocci, Agata Ciabattoni, and Guido Governatori. "A Normative Supervisor for Reinforcement Learning Agents." In Automated Deduction – CADE 28. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_32.

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AbstractWe introduce a modular and transparent approach for augmenting the ability of reinforcement learning agents to comply with a given norm base. The normative supervisor module functions as both an event recorder and real-time compliance checker w.r.t. an external norm base. We have implemented this module with a theorem prover for defeasible deontic logic, in a reinforcement learning agent that we task with playing a “vegan” version of the arcade game Pac-Man.

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Hermann, Gilles, Patrice Wira, and Jean-Philippe Urban. "Modular Learning Schemes for Visual Robot Control." In Biomimetic Neural Learning for Intelligent Robots. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11521082_20.

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Conference papers on the topic "Modular Learning"

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Dhakan, Paresh, Kathryn Elizabeth Merrick, Inaki Rano, and Nazmul Haque Siddique. "Modular Continuous Learning Framework." In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2018. http://dx.doi.org/10.1109/devlrn.2018.8761008.

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Hong, Zhang-Wei, Yu-Ming Chen, Hsuan-Kung Yang, et al. "Virtual-to-Real: Learning to Control in Visual Semantic Segmentation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/682.

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Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform. Unfortunately, the reality gap between synthetic and real visual data prohibits direct migration of the models trained in virtual worlds to the real world. This paper proposes a modular architecture for tackling the virtual-to-real problem. The proposed architecture separates the learning model into a perception module and a control policy module, and uses semantic image segmentation as the meta representation for relating these two modules. The perception module translates the perceived RGB image to semantic image segmentation. The control policy module is implemented as a deep reinforcement learning agent, which performs actions based on the translated image segmentation. Our architecture is evaluated in an obstacle avoidance task and a target following task. Experimental results show that our architecture significantly outperforms all of the baseline methods in both virtual and real environments, and demonstrates a faster learning curve than them. We also present a detailed analysis for a variety of variant configurations, and validate the transferability of our modular architecture.

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Schweikardt, Eric, and Mark D. Gross. "Learning about Complexity with Modular Robots." In 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning. IEEE, 2008. http://dx.doi.org/10.1109/digitel.2008.49.

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Hrycej, T. "A modular architecture for efficient learning." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137625.

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Takeda, Manabu, Kazushi Ikeda, and Tetsuo Furukawa. "Learning properties of Modular Network SOMs." In SICE 2008 - 47th Annual Conference of the Society of Instrument and Control Engineers of Japan. IEEE, 2008. http://dx.doi.org/10.1109/sice.2008.4655078.

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Zedong Tang, Maoguo Gong, and Mingyang Zhang. "Evolutionary multi-task learning for modular extremal learning machine." In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017. http://dx.doi.org/10.1109/cec.2017.7969349.

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Chitnis, Rohan, Leslie Pack Kaelbling, and Tomas Lozano-Perez. "Learning Quickly to Plan Quickly Using Modular Meta-Learning." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794342.

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Yumun, Chatchai, Mongkol Konghirun, and Kanokvate Tungpimolrut. "Modular Learning Laboratory of AC Servo Drive." In 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2008. http://dx.doi.org/10.1109/ecticon.2008.4600617.

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Erlingsson, E., G. Cavallaro, A. Galonska, M. Riedel, and H. Neukirchen. "Modular supercomputing design supporting machine learning applications." In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2018. http://dx.doi.org/10.23919/mipro.2018.8400031.

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Sedova, Anastasiia, Andreas Stephan, Marina Speranskaya, and Benjamin Roth. "Knodle: Modular Weakly Supervised Learning with PyTorch." In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.repl4nlp-1.12.

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Reports on the topic "Modular Learning"

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Jesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430017.

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Harrison, Thomas J. Development of the Mathematics of Learning Curve Models for Evaluating Small Modular Reactor Economics. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1185415.

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Harrison, T. Development of the Mathematics of Learning Curve Models for Evaluating Small Modular Reactor Economics. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1163909.

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Lovett, C. Denver. Manufacturing technology learning modules - sharing resources for school outreach. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6313.

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Hurwitz, David, Rachel Adams, H. Benjamin Mason, Kamilah Buker, and Richard Slocum. Innovation in the classroom : A transportation geotechnics application of desktop learning modules to promote inductive learning. Oregon State University, 2017. http://dx.doi.org/10.5399/osu/1113.

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Lamar, Traci A. M. Teaching Critical Color Concepts through an Online Learning Module. Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-1915.

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Hirayama, Yuji. A PROLOG Lexical Phrase Computer Assisted Language Learning Module. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7173.

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Gilman, Jessica H. Improving Learning with the Critical Thinking Paradigm: MIBOLC Modules A and B. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada494890.

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Schneider, Sarah, Daniel Wolf, and Astrid Schütz. Workshop for the Assessment of Social-Emotional Competences : Application of SEC-I and SEC-SJT. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49180.

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The modular workshop offers a science-based introduction to the concept of social-emotional competences. It focuses on the psychological assessment of such competences in in institutions specialized in the professional development of people with learning disabilities. As such, the workshop is primarily to be understood as an application-oriented training programme for professionals who work in vocational education and use (or teach the usage of) the assessment tools SEC-I and SEC-SJT (Inventory and Situational Judgment Test for the assessment of social-emotional competence in young people with (sub-) clinical cognitive or psychological impairment) which were developed at the University of Bamberg. The workshop comprises seven subject areas that can be flexibly put together as required: theoretical basics and definitions of social-emotional competence, the basics of psychological assessment, potential difficulties in its use, usage of the self-rating scale, the situational judgment test, the observer-rating scale, and objective observation of behaviour. The general aim of this workshop is to learn how to use and apply the assessment tools in practical settings.

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Shen, Chaopeng, Forrest Hoffman, and Chonggang Xu. Integrated parameter and process learning for hydrologic and biogeochemical modules in Earth System Models. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769724.

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