Relevant bibliographies by topics / Operant learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Operant learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Operant learning"

1

Brembs, Björn. "Operant Reward Learning inAplysia." Current Directions in Psychological Science 12, no. 6 (2003): 218–21. http://dx.doi.org/10.1046/j.0963-7214.2003.01265.x.

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Leslie, Julian C. "Selection in operant learning may fit a general model." Behavioral and Brain Sciences 24, no. 3 (2001): 542–43. http://dx.doi.org/10.1017/s0140525x01374162.

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The generic account of selection proposed by Hull et al. readily fits operant learning where, by comparison with natural selection, the process is well understood but little is known about the mechanism. Objections within psychology, that operant learning ignores internal processes, fail to recognise the general significance of behaviour-environment interactions. Variation within operant response classes requires further investigation.
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Neuringer, Allen, Chris Deiss, and Gene Olson. "Reinforced variability and operant learning." Journal of Experimental Psychology: Animal Behavior Processes 26, no. 1 (2000): 98–111. http://dx.doi.org/10.1037/0097-7403.26.1.98.

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Staddon, J. E. R., and Y. Zhang. "Response selection in operant learning." Behavioural Processes 20, no. 1-3 (1989): 189–97. http://dx.doi.org/10.1016/0376-6357(89)90022-3.

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Cai, Jian Xian, Hong Rui Ma, and Quan Sheng Wang. "Bionic Autonomous Learning Mechanism Study Based on Automaton and Applied on Robot." Applied Mechanics and Materials 58-60 (June 2011): 1408–14. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1408.

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Under the learning automaton frame a bionic self-learning automaton which has autonomous learning mechanism is constructed based on conditioned reflex theory. The bionic self-learning automaton is a 5-tupe and mainly contains: 1) inner set, mainly are perception state set and operant set; 2) oriented core, guide the direction of autonomous learning; 3) learning core, the primitives of learning unit; 4) autonomous learning unit, implement learning computation; 5) operant entropy, measuring the self-organized degree of bionic self-learning automaton. Theory analysis is made for the designed bion
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Bulla, Andrew J., and Michael Woodcock. "Ratsketball: Using low-cost 3D-printed operant chambers to probe for generative learning." Revista Mexicana de Análisis de la Conducta 50, no. 1 (2024): 67–84. http://dx.doi.org/10.5514/rmac.v50.i1.88703.

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The cost of operant chambers used to teach students basic concepts and principles of behavior analysis and learning has increased across time, adding a barrier for instructors who wish to incorporate animal models within their laboratory courses, instead using virtual and analog models. Researchers have begun to investigate the use of 3D-printed operant chambers in laboratory classes as a low-cost alternative to traditional operant chambers. The current paper extends the literature on low-cost alternatives and provides an overview of the methodology to create and use 3D-printed operant chamber
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Gutnisky, D. A., and B. S. Zanutto. "Cooperation in the Iterated Prisoner's Dilemma Is Learned by Operant Conditioning Mechanisms." Artificial Life 10, no. 4 (2004): 433–61. http://dx.doi.org/10.1162/1064546041766479.

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The prisoner's dilemma (PD) is the leading metaphor for the evolution of cooperative behavior in populations of selfish agents. Although cooperation in the iterated prisoner's dilemma (IPD) has been studied for over twenty years, most of this research has been focused on strategies that involve nonlearned behavior. Another approach is to suppose that players' selection of the preferred reply might be enforced in the same way as feeding animals track the best way to feed in changing nonstationary environments. Learning mechanisms such as operant conditioning enable animals to acquire relevant c
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Yuli, Yunita Nita, Alfitra Firmansyah, Muqowim Muqowim, and Muhammad Alwi Nurdin. "APPLICATION OF B.F. SKINNER'S BEHAVIORISM LEARNING THEORY IN ISLAMIC EDUCATION LEARNING FOR HIGH SCHOOL STUDENTS." JURNAL ILMIAH DIDAKTIKA: Media Ilmiah Pendidikan dan Pengajaran 25, no. 1 (2024): 27. http://dx.doi.org/10.22373/jid.v25i1.24233.

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This study aimed to know and describe the application of B.F Skinner's Operant Conditioning Behaviorism Learning Theory in Islamic Education learning at high school students and to know students' responses. In this study, the researchers described the forms of stimulus provision and the principles of Operant Conditioning B.F Skinner applied in Islamic Education learning. This research used qualitative methods. The data collection techniques used observation, interviews, and documentation. While data analysis is done with several stages, namely data reduction, data presentation, and conclusion
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Hölzl, Rupert, Dieter Kleinböhl, and Ellena Huse. "Implicit operant learning of pain sensitization." Pain 115, no. 1 (2005): 12–20. http://dx.doi.org/10.1016/j.pain.2005.01.026.

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Gewirtz, Jacob L. "Infant operant learning in natural settings." Infant Behavior and Development 19 (April 1996): 86. http://dx.doi.org/10.1016/s0163-6383(96)90141-1.

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Dissertations / Theses on the topic "Operant learning"

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Reed, Phillip. "Free-operant schedules with signalled reinforcement." Thesis, University of York, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304357.

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Stephens, Robert. "Associative and operant learning in unilateral visual neglect." Thesis, University of Birmingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485991.

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Visual neglect of stimuli on the left side of space typically occurs following damage to the right parietal lobe following cardiovascular accident or 'stroke' and is generally understood to arise from an attentional bias towards one side ofspace. It is proposed that the more automatic mechanisms of normal visual attention, those responsive to reward/threat are typically spared in this syndrome and may be utilised to modulate visual search behaviour. By applying a behavioural shaping procedure to a visual search task, it was found that the search behaviour of two out ofthree participants with c
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Oddsson, Finnur. "Fluency and controlled-operant training methods." Morgantown, W. Va. : [West Virginia University Libraries], 1998. http://etd.wvu.edu/templates/showETD.cfm?recnum=222.

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Thesis (M.A.)--West Virginia University, 1998.<br>Title from document title page. Document formatted into pages; contains viii, 79 p. : ill. Includes abstract. Includes bibliographical references (p. 66-68).
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Seymour, Kail H. "The Effects of Reinforcing Operant Variability on Task Acquisition." Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3273/.

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Neuringer, Deiss, and Olson (2000) was replicated and extended to determine the effect of variability contingencies on task acquisition for twelve 7-9 year old children. Subjects first learned to press a computer's shift keys with increasing response variation. Each subject was then exposed to one of three experimental conditions during which they received a point for target responses. Variability condition subjects received additional points on a variable interval schedule for nontarget responses occurring less than 3% of the time. The any condition subjects received additional points on a va
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Delgado, Diana M. "An interpretation of operant learning in terms of substitution processes /." abstract and full text PDF (UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3342545.

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Thesis (Ph. D.)--University of Nevada, Reno, 2008.<br>"December 2008." Includes bibliographical references (leaves 124-135). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2009]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.
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Stewart, Paul H. "Equine Operant Conditioning: Autoshaping, Observational Learning, and Discriminative Stimulus Intensity." DigitalCommons@USU, 1992. https://digitalcommons.usu.edu/etd/3839.

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This thesis is compromised of three studies in which basic principles of operant conditioning were applied to horses. Autoshaping was examined as a method for horse training. Observational learning was investigated to confirm that naive horses can, in fact, acquire novel behavior by observing experienced horses, and the rate of acquisition with observation is more rapid than spontaneous responding without observation. A third study examined the effect of discriminative stimulus intensity on the acquisition rate of novel behavior. All subjects learned to use an operant conditioning device. Subj
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Bertolucci, Franco. "Operant and classical learning in Drosophila melanogaster: the ignorant gene (ign)." kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2009/3398/.

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Duarte, Myra. "The effects of immediate versus delayed reinforcement on infant operant learning." FIU Digital Commons, 2002. http://digitalcommons.fiu.edu/etd/3089.

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Two experiments examined operant leg kick responses to a panel by six 3-month-old infants under baseline, immediate reinforcement, delayed reinforcement, and yoked-control conditions using a discrete trial procedure and a single-subject repeated-measures design. Two infants participated in the first experiment and four infants participated in the second experiment. The research design of Experiment I was baseline (A), 5s delay of reinforcement (C), yoked-control (A'), and immediate reinforcement (B). There were two experimental orders in the second experiment. The first order consisted of base
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Datta, Udita. "Characterization of Drug Reward in an Invertebrate Model System Using Operant Conditioning Paradigms." Bowling Green State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1429213190.

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Bhimani, Rohan. "Operant Place Aversion in the Rusty Crayfish, Orconectes rusticus." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1415978513.

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Books on the topic "Operant learning"

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Staveley-Taylor, Helen. Classical and operant conditioning. Films for the Humanities & Sciences, 2004.

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Rumbaugh, Duane M. Respondents, operants, and emergents: Toward an integrated perspective on behavior. National Aeronautics and Space Administration, 1997.

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Robert, Kreitner, and Luthans Fred, eds. Organizational behavior modification and beyond: An operant and social learning approach. Scott, Foresman, 1985.

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John, Lutz. Introduction to learning & memory. Brooks/Cole Pub. Co., 1994.

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Lutz, John. Introduction to learning & memory. Brooks-Cole, 1994.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum Associates, 1989.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum Associates, 1989.

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B, Klein Stephen, and Mowrer Robert R. 1956-, eds. Contemporary learning theories. L. Erlbaum, 1989.

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Hammerl, Marianne. Effekte signalisierter Verstärkung: Ein experimenteller Beitrag zu den Grundlagen der Lernpsychologie. Roderer, 1991.

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Anne, Rehfeldt Ruth, and Barnes-Holmes Yvonne, eds. Derived relational responding: Applications for learners with autism and other developmental disabilities. New Harbinger Publications, 2009.

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Book chapters on the topic "Operant learning"

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Critchfield, Thomas S. "Operant Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1045.

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Toates, Frederick. "Operant Behavior." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_992.

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Mazur, James E., and Amy L. Odum. "Basic Principles of Operant Conditioning." In Learning and Behavior, 9th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003215950-5.

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Mazur, James E., and Amy L. Odum. "Theories and Research on Operant Conditioning." In Learning and Behavior, 9th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003215950-8.

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Domjan, Michael, and Andrew R. Delamater. "Instrumental or operant conditioning." In The essentials of conditioning and learning (5th ed.). American Psychological Association, 2023. http://dx.doi.org/10.1037/0000363-007.

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Rasmussen, Erin B., Casey J. Clay, W. David Pierce, and Carl D. Cheney. "Reinforcement and Extinction of Operant Behavior." In Behavior Analysis and Learning, 7th ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003202622-4.

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Pierce, W. David, and Carl D. Cheney. "Reinforcement and Extinction of Operant Behavior." In Behavior Analysis and Learning. Routledge, 2017. http://dx.doi.org/10.4324/9781315200682-4.

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Neuringer, Allen. "Reinforced Variability and Operant Behavior." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1039.

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Johnson, M. H., and G. Horn. "Brain Lesions, Imprinting, and Operant Learning." In Brain Plasticity, Learning, and Memory. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4684-5003-3_77.

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Weisman, R. G. "On the Role of the Reinforcer in Associative Learning." In Operant-Pavlovian Interactions. Routledge, 2021. http://dx.doi.org/10.4324/9781003150404-1.

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Conference papers on the topic "Operant learning"

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Morris, Evan W., Jonah A. Padawer-Curry, Zachary C. Lieske, Annie R. Bice, and Adam Q. Bauer. "Evolution of spatiotemporal cortical dynamics in mice learning operant motor tasks." In Clinical and Translational Neurophotonics 2025, edited by Jana M. Kainerstorfer, Erin M. Buckley, and Vivek J. Srinivasan. SPIE, 2025. https://doi.org/10.1117/12.3042052.

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Kamarudzaman, Muhammad Faris Bin, Prawit Buayai, Yin Suan Tan, Latifah Munirah Kamarudin, and Xiaoyang Mao. "Versatile and Easy-to-Operate Grading System for High-Grade Table Grapes: Leveraging Deep Learning, Computer Vision, and IoT." In 2024 International Conference on Cyberworlds (CW). IEEE, 2024. https://doi.org/10.1109/cw64301.2024.00037.

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Huang, Jing, Xiaogang Ruan, Lei Li, Ruoyan Wei, Qingwu Fan, and Xuan Wu. "Operant conditioning learning model based on BP network." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896407.

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Hong-ge, Ren, and Ruan Xiao-gang. "Self-Learning of Robot Based on Skinner's Operant Conditioning." In 2009 International Conference on Information Technology and Computer Science (ITCS 2009). IEEE, 2009. http://dx.doi.org/10.1109/itcs.2009.253.

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Lew, Sergio, Hernan G. Rey, and B. Silvano Zanutto. "Neuronal mechanisms underlying exploration-exploitation strategies in operant learning." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706987.

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Zhang, Xiaoping, Xiaogang Ruan, Yao Xiao, and Jing Huang. "A self-learning sensorimotor model based on operant conditioning theory." In 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2015. http://dx.doi.org/10.1109/iaeac.2015.7428618.

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Gao, Yuanyuan, Xiaogang Ruan, and Bin Li. "A probabilistic fuzzy controller with operant learning for robot navigation." In 2012 10th World Congress on Intelligent Control and Automation (WCICA 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcica.2012.6357901.

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"Quantum Probability in Operant Conditioning - Behavioral Uncertainty in Reinforcement Learning." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004903205480551.

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Gutnisky, D. A., R. Zelmann, and B. S. Zanutto. "Multiagent team formation performed by operant learning: an animat approach." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247228.

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Xiaogang Ruan, Yuanyuan Gao, and Hongjun Song. "Operant conditioning automata model and a bionic autonomous learning process." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5358370.

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Reports on the topic "Operant learning"

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Orbán, Levente. Associative Learning in Psychology: Classical and Operant Conditioning Revisited. Orban Foundation, 2025. https://doi.org/10.69642/5323.

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Learning is a central concept in psychology, governing how organisms adapt to their environments. This paper revisits foundational theories of associative learning, with an emphasis on classical conditioning, as discovered by Ivan Pavlov, and operant conditioning, as developed by B.F. Skinner. Drawing from a transcript of a live undergraduate lecture, we explore theoretical models, empirical evidence, practical examples, and mathematical structures underlying these learning mechanisms. The aim is to distill key insights while highlighting the contemporary relevance of behaviorist principles in
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Brembs, Björn. What is the function of FoxP in operant self-learning? [project]. ThinkLab, 2015. http://dx.doi.org/10.15363/thinklab.11.

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Brembs, Björn. What is the function of FoxP in operant self-learning? [proposal]. ThinkLab, 2015. http://dx.doi.org/10.15363/thinklab.a8.

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Harlow, Bob. The Tech Effect: Seattle Forges a Culture of Learning. Bob Harlow Research and Consulting, LLC, 2015. http://dx.doi.org/10.59656/a-ad6065.002.

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JOSI, Editor. Artificial Intelligence and Machine Learning: Transforming Industrial Optimization. Industrial Engineering Department, Faculty of Engineering, Universitas Andalas, 2025. https://doi.org/10.25077/03032025.

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The integration of artificial intelligence (AI) and machine learning (ML) into industrial systems is rapidly reshaping the way industries operate, optimize, and innovate. As industries grow more complex and data-driven, the ability to harness AI and ML technologies offers unprecedented efficiency, precision, and adaptability. In the pursuit of optimization, these technologies are no longer just experimental tools but have become essential drivers of transformation.
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Rduner, Tim G. J., and Helen Toner. Key Concepts in AI Safety: Specification in Machine Learning. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20210031.

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This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learn
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Shawoo, Zoha, Inès Bakhtaoui, Lisa Schultheiß, Nusrat Naushin, and Lina Ahmed. Operationalizing the Loss and Damage Fund: learning from the perspectives of funders and potential recipients. Stockholm Environment Institute, 2023. http://dx.doi.org/10.51414/sei2023.043.

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This brief summarizes findings of research undertaken to examine ways to design and operate the new Loss and Damage Fund to best achieve its aims of helping vulnerable people and places adapt to the impacts of climate change.
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Ibrahim, Mohamed. Covid-19: Need for Agility in Islamic Finance Education in Saudi Arabia. Islamic Development Bank Institute, 2020. http://dx.doi.org/10.55780/re24011.

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The world of learning and education is changing. The Covid-19 pandemic has had an unprecedented impact on organizations, individuals, economies and societies. Universities, educational institutes, and learning providers have had to respond to a rapidly evolving landscape, changing how and where they operate, as well as reconsidering the services they offer (CIPD, 2020).
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Rabellotti, Roberta, and Carlo Pietrobelli. Global Value Chains Meet Innovation Systems : Are There Learning Opportunities for Developing Countries? Inter-American Development Bank, 2010. http://dx.doi.org/10.18235/0011001.

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This paper addresses two research questions: How do learning mechanisms operate in different types of global value chains? What is the supporting role of the innovation system in GVC-driven learning and innovation processes? Empirical evidence is used from the authors' research as well as secondary sources. Given the highly differentiated reality behind the sketchy and simplistic term "developing countries," the paper focuses on a particular group of middle-income developing countries: Brazil, Taiwan and Mexico.
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust
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