Relevant bibliographies by topics / Reinforcement Schedules

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

Academic literature on the topic 'Reinforcement Schedules'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Reinforcement Schedules"

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Latham, Gary P., and Vandra L. Huber. "Schedules of Reinforcement:." Journal of Organizational Behavior Management 12, no. 1 (January 25, 1991): 125–49. http://dx.doi.org/10.1300/j075v12n01_06.

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Pérez, Omar D., Michael RF Aitken, Peter Zhukovsky, Fabián A. Soto, Gonzalo P. Urcelay, and Anthony Dickinson. "Human instrumental performance in ratio and interval contingencies: A challenge for associative theory." Quarterly Journal of Experimental Psychology 72, no. 2 (January 1, 2018): 311–21. http://dx.doi.org/10.1080/17470218.2016.1265996.

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Associative learning theories regard the probability of reinforcement as the critical factor determining responding. However, the role of this factor in instrumental conditioning is not completely clear. In fact, free-operant experiments show that participants respond at a higher rate on variable ratio than on variable interval schedules even though the reinforcement probability is matched between the schedules. This difference has been attributed to the differential reinforcement of long inter-response times (IRTs) by interval schedules, which acts to slow responding. In the present study, we used a novel experimental design to investigate human responding under random ratio (RR) and regulated probability interval (RPI) schedules, a type of interval schedule that sets a reinforcement probability independently of the IRT duration. Participants responded on each type of schedule before a final choice test in which they distributed responding between two schedules similar to those experienced during training. Although response rates did not differ during training, the participants responded at a lower rate on the RPI schedule than on the matched RR schedule during the choice test. This preference cannot be attributed to a higher probability of reinforcement for long IRTs and questions the idea that similar associative processes underlie classical and instrumental conditioning.
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Mawhinney, Thomas C. "Trigger Pulling for Monetary Reinforcements by a Single Subject during Ninety-Nine Ten-Minute Sessions." Psychological Reports 75, no. 2 (October 1994): 812–14. http://dx.doi.org/10.2466/pr0.1994.75.2.812.

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Reinforcement maximization by identifying and following switching rules that occurred on conFR/VI-10 sec. reinforcement schedules did not occur when the subject experienced conFR/VI-20 sec. reinforcement schedules. Exclusive preference for the schedule with the lower valued N on conFR-N/FR-N schedules occurred as predicted by both matching and maximization theories of operant choice behavior. Additional research is required to assess the reliability of the phenomenon observed and factors upon which its occurrence may depend.
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Reed, Phil. "Human free-operant performance varies with a concurrent task: Probability learning without a task, and schedule-consistent with a task." Learning & Behavior 48, no. 2 (January 2, 2020): 254–73. http://dx.doi.org/10.3758/s13420-019-00398-1.

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AbstractThree experiments examined human rates and patterns of responding during exposure to various schedules of reinforcement with or without a concurrent task. In the presence of the concurrent task, performances were similar to those typically noted for nonhumans. Overall response rates were higher on medium-sized ratio schedules than on smaller or larger ratio schedules (Experiment 1), on interval schedules with shorter than longer values (Experiment 2), and on ratio compared with interval schedules with the same rate of reinforcement (Experiment 3). Moreover, bout-initiation responses were more susceptible to influence by rates of reinforcement than were within-bout responses across all experiments. In contrast, in the absence of a concurrent task, human schedule performance did not always display characteristics of nonhuman performance, but tended to be related to the relationship between rates of responding and reinforcement (feedback function), irrespective of the schedule of reinforcement employed. This was also true of within-bout responding, but not bout-initiations, which were not affected by the presence of a concurrent task. These data suggest the existence of two strategies for human responding on free-operant schedules, relatively mechanistic ones that apply to bout-initiation, and relatively explicit ones, that tend to apply to within-bout responding, and dominate human performance when other demands are not made on resources.
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Shah, K., C. M. Bradshaw, and E. Szabadi. "Performance of Humans in Concurrent Variable-Ratio Variable-Ratio Schedules of Monetary Reinforcement." Psychological Reports 65, no. 2 (October 1989): 515–20. http://dx.doi.org/10.2466/pr0.1989.65.2.515.

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Four women pressed a button in five two-component concurrent variable-ratio variable-ratio ( conc VR VR) schedules of monetary reinforcement. There was no consistent tendency towards “probability matching” (distribution of responses between the two components in proportion to the relative probabilities of reinforcement); three of the four subjects showed exclusive preference for the schedule associated with the higher probability of reinforcement. These results are similar to results previously obtained with pigeons and rats in concurrent VR VR schedules.
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Nuijten, Raoul, Pieter Van Gorp, Alireza Khanshan, Pascale Le Blanc, Astrid Kemperman, Pauline van den Berg, and Monique Simons. "Health Promotion through Monetary Incentives: Evaluating the Impact of Different Reinforcement Schedules on Engagement Levels with a mHealth App." Electronics 10, no. 23 (November 26, 2021): 2935. http://dx.doi.org/10.3390/electronics10232935.

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Background: Financial rewards can be employed in mHealth apps to effectively promote health behaviors. However, the optimal reinforcement schedule—with a high impact, but relatively low costs—remains unclear. Methods: We evaluated the impact of different reinforcement schedules on engagement levels with a mHealth app in a six-week, three-arm randomized intervention trial, while taking into account personality differences. Participants (i.e., university staff and students, N = 61) were awarded virtual points for performing health-related activities. Their performance was displayed via a dashboard, leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed using a fixed schedule in the first study arm, and a variable schedule in the other arms. Furthermore, payouts were immediate in the first two arms, whereas payouts in the third arm were delayed. Results: All three reinforcement schedules had a similar impact on user engagement, although the variable schedule with immediate payouts was reported to have the lowest cost per participant. Additionally, the impact of financial rewards was affected by personal characteristics. Especially, individuals that were triggered by the rewards had a greater ability to defer gratification. Conclusion: When employing financial rewards in mHealth apps, variable reinforcement schedules with immediate payouts are preferred from the perspective of cost and impact.
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Schuett, Mary Andrews, and J. Michael Leibowitz. "Effects of Divergent Reinforcement Histories upon Differential Reinforcement Effectiveness." Psychological Reports 58, no. 2 (April 1986): 435–45. http://dx.doi.org/10.2466/pr0.1986.58.2.435.

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The effectiveness of differential reinforcement techniques in reducing lever-pressing was studied as a function of natural reinforcement history and prescribed schedule. Based upon a prebaseline, 30 children with natural high rates of responding and 30 children with natural low rates of responding were reinforced for tapping an assigned key for 15 min. on either a differential reinforcement of low rate (drl 5“) or a differential reinforcement of high rate (Conjunctive VR 10-drh 5”) schedule of reinforcement. Responding on the other key was then reinforced for 15 min. on a variable ratio (VR 35) schedule utilizing one of three differential reinforcement techniques to eliminate the previously taught response. Findings indicated that a child's natural history significantly influences subsequent rates of responding. Prescribed divergent schedules effected changes in responding only while the child was being reinforced on that schedule. The differential reinforcement techniques did not produce significant differences between subjects' performance on the new key but did affect responding on the previously reinforced key.
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Steinhauer, Gene D. "Behavioral Contrast on Mixed Schedules." Psychological Reports 78, no. 2 (April 1996): 673–74. http://dx.doi.org/10.2466/pr0.1996.78.2.673.

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Keypecking by 4 pigeons was studied on mixed schedules of reinforcement. Positive behavioral contrast was found when the schedule was shifted from Mixed VI VI to Mixed VI Extinction only when the VI schedule value was small relative to the component duration.
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Ferster, C. B. "SCHEDULES OF REINFORCEMENT WITH SKINNER." Journal of the Experimental Analysis of Behavior 77, no. 3 (May 2002): 303–11. http://dx.doi.org/10.1901/jeab.2002.77-303.

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Morse, W. H., and P. B. Dews. "FOREWORD TO SCHEDULES OF REINFORCEMENT." Journal of the Experimental Analysis of Behavior 77, no. 3 (May 2002): 313–17. http://dx.doi.org/10.1901/jeab.2002.77-313.

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Dissertations / Theses on the topic "Reinforcement Schedules"

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Abdel-Jalil, Awab. "Stimulus Control Effects of Changes in Schedules of Reinforcement." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707360/.

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Sometimes, changes in consequences are accompanied by a clear stimulus change explicitly arranged by the experimenter. Other times when new consequences are in effect, there is little or no accompanying stimulus change explicitly arranged by the experimenter. These differences can be seen in the laboratory as multiple (signaled) schedules and mixed (unsignaled) schedules. The current study used college students and a single-subject design to examine the effects of introducing signaled and unsignaled schedules, and the transitions between them. In one phase, a card was flipped from purple to white every time the schedule was switched from VR-3 to FT-10. In another phase, the schedule still changed periodically, but the card always remained on the purple side. Results showed that the participants' responding was controlled by the schedule of reinforcement, by the color of the card, or both. These results suggest that changes in patterns of reinforcement lead to changes in stimulus control. In addition, the stimulus control for a behavior can come from several different sources. During teaching, it may facilitate the development of stimulus control to change the environment when a new behavior is required.
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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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Bullock, Christopher. "Stimulus functions in token-reinforcement schedules." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0013785.

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Toal, William. "Microanalysis of performance under interval schedules of reinforcement." Thesis, University of Ulster, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328212.

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Palmer, Ashlyn. "Using Concurrent Schedules of Reinforcement to Decrease Behavior." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062900/.

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We manipulated delay and magnitude of reinforcers in two concurrent schedules of reinforcement to decrease a prevalent behavior while increasing another behavior already in the participant's repertoire. The first experiment manipulated delay, implementing a five second delay between the behavior and delivery of reinforcement for a behavior targeted for decrease while no delay was implemented after the behavior targeted for increase. The second experiment manipulated magnitude, providing one piece of food for the behavior targeted for decrease while two pieces of food were provided for the behavior targeted for increase. The experiments used an ABAB reversal design. Results suggest that behavior can be decreased without the use of extinction when contingencies favor the desirable behavior.
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Bullock, Christopher. "Second-order schedules of token reinforcement combined effects of token-production and exchange-schedule manipulations /." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000654.

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Wade, Tammy R. "Effects of schedule segmentation on pausing and escape in the transitions between favorable and unfavorable schedules of reinforcement." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3383.

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Thesis (Ph. D.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains vii, 74 p. : ill. Includes abstract. Includes bibliographical references (p. 72-74).
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Barnes, Dermot. "Schedules of reinforcement and human behaviour : a contextualistic perspective." Thesis, University of Ulster, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292805.

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基燦, 宋., and Kichan Song. "Effects of different reinforcement schedules on neuronal operant conditioning." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13118640/?lang=0, 2019. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13118640/?lang=0.

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Bonem, Elliott J. "The Highest Local Density of Reinforcement Controls Overall Post-Reinforcement Pause Duration on Ratio Schedules." DigitalCommons@USU, 1988. https://digitalcommons.usu.edu/etd/5604.

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A series of experiments were conducted with pigeons to investigate the variables responsible for differential postreinforcement pause (PRP) durations found on ratio schedules. In Experiment I, behavior on fixed-ratio (FR) and variable-ratio (VR) schedules were compared to behavior evoked by two interpolated schedules. The addition of a single FR 1 component to the FR 50 baseline schedule reduced the overall PRP to a duration comparable to that found on the VR 50 schedule. The addition of both an FR 1 and an FR 215 component to an FR 50 baseline reduced PRP and IRT durations below those on a VR 50 schedule. Experiments II and III were designed to isolate the conditions under which the smallest ratio component exerts predominant control over PRP duration. The results of Experiment II demonstrated that a local increase in reinforcement density was a necessary, but not sufficient condition for reducing median PRP duration. That is, exposure to a response-independnt increase in reinforcement density attenuated, but did not eliminate the reduction in median PRP duration associated with the interpolated FR 1 component. The results of Experiment III demonstrated that neither random session location of the FR 1 component nor unsignaled presentation of the FR 1 component were necessary conditions for reducing the duration of the PRP. That is, a brief, response-dependent increase in reinforcement density was a sufficient condition for reducing PRP duration given a subject free from historical exposure to response-independent reinforcement. It was concluded that the difference in PRP duration produced by two, comparably-sized, fixed- and variable-ratio schedules is a function of the size of the smallest ratio component present in the reinforcement schedule. More generally, the highest local density of reinforcement controls the overall duration of the PRP on a response-dependent, ratio schedule.
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Books on the topic "Reinforcement Schedules"

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Toal, William. Microanalysis of performance under interval schedules of reinforcement. [S.l: The Author], 1988.

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Barnes, Dermot. Schedules of reinforcement and human behaviour: A contextualistic perspective. [s.l: The Author], 1990.

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Kerr, Kenneth. Schedules of reinforcement and stimulus equivalence: An integrative approach to rule-governed behaviour. [S.l: The Author], 1996.

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Dawson, Gerard Raphael. Animal timing: A micro-analysis of the differential-reinforcement-of-low-rates schedule. [S.l: TheAuthor], 1988.

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Skinner, B. F., and C. B. Ferster. Schedules of Reinforcement. Martino Fine Books, 2022.

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Allen, Ronald F. The reinforcing effects of general illumination during chained schedules of food presentation. 1990.

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Schedules of Reinforcement (B. F. Skinner Reprint Series) (B. F. Skinner Reprint Series). Copley Publishing Group, 1997.

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(Editor), Michael L. Commons, and John A. Nevin (Editor), eds. Discriminative Properties of Reinforcement Schedules: Quantitative Analyses of Behavior, Volume I (Quantitative Analyses of Behavior Series). Lawrence Erlbaum, 1985.

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Files, Forrest J. Effects of repeated cocaine administration on responding under multiple fixed-ratio schedules of reinforcement. 1992.

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Schaal, David W. Responding of pigeons under variable-interval schedules of signaled-delayed reinforcement: Effects of delay-signal duration. 1988.

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Book chapters on the topic "Reinforcement Schedules"

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Lattal, Kennon A. "Schedules of Reinforcement." In Encyclopedia of the Sciences of Learning, 2929–33. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_990.

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Evans, David C. "Schedules of Reinforcement." In Bottlenecks, 157–68. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2580-6_15.

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Dews, P. B. "Schedules of Reinforcement." In Ciba Foundation Symposium - Animal Behaviour and Drug Action, 191–206. Chichester, UK: John Wiley & Sons, Ltd., 2008. http://dx.doi.org/10.1002/9780470719329.ch12.

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Derenne, Adam. "Schedules of Reinforcement." In Encyclopedia of Animal Cognition and Behavior, 1–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-47829-6_1555-1.

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Hackenberg, Timothy D. "Schedules of reinforcement." In Encyclopedia of psychology, Vol. 7., 156–58. Washington: American Psychological Association, 2000. http://dx.doi.org/10.1037/10522-063.

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Derenne, Adam. "Schedules of Reinforcement." In Encyclopedia of Animal Cognition and Behavior, 6248–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_1555.

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Zeiler, Michael. "Schedules of Reinforcement." In Handbook of Operant Behavior, 201–32. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003256670-9.

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Pierce, W. David, and Carl D. Cheney. "Schedules of Reinforcement." In Behavior Analysis and Learning, 135–74. Sixth edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315200682-5.

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Rasmussen, Erin B., Casey J. Clay, W. David Pierce, and Carl D. Cheney. "Schedules of Reinforcement." In Behavior Analysis and Learning, 149–86. 7th ed. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003202622-5.

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Baum, William M. "Random and Systematic Foraging, Experimental Studies of Depletion, and Schedules of Reinforcement." In Foraging Behavior, 587–607. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2_21.

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Conference papers on the topic "Reinforcement Schedules"

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"Reinforcement Learning for Multi-purpose Schedules." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004187202030209.

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Pecenin, Marcelo, André Murbach Maidl, and Daniel Weingaertner. "Optimization of Halide Image Processing Schedules with Reinforcement Learning." In XX Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/wscad.2019.8655.

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Writing efficient image processing code is a very demanding task and much programming effort is put into porting existing code to new generations of hardware. Besides, the definition of what is an efficient code varies according to the desired optimization target, such as runtime, energy consumption or memory usage. We present a semi-automatic schedule generation system for the Halide DSL that uses a Reinforcement Learning agent to choose a set of scheduling options that optimizes the runtime of the resulting application. We compare our results to the state of the art implementations of three Halide pipelines and show that our agent is able to surpass hand-tuned code and Halide’s auto-scheduler on most scenarios for CPU and GPU architectures.
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Chaudhary, Deepakshi. "WHAT WILL DRIVE CONSUMERS TO BUY GREEN PRODUCTS: A STUDY OF REINFORCEMENT SCHEDULES TO BE APPLIED IN MARKETING." In 42nd International Academic Conference, Rome. International Institute of Social and Economic Sciences, 2018. http://dx.doi.org/10.20472/iac.2018.042.006.

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Chambers, David, Thorsten Lammers, and Kun Yu. "Dynamic Scheduling Techniques in Cloud Manufacturing – An Exploration of Deep Reinforcement Learning as a Critical Opportunity for Future Research." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001625.

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Objective: Determine the state-of-the-art in dynamic scheduling techniques for cloud manufacturing.Significance: This paper firmly establishes the underexplored technique of Deep Reinforcement Learning as the state-of-the-art for dynamic scheduling in cloud manufacturing, exposes a significant gap in the literature, and sets out critical future research objectives.Abstract:For many years, metaheuristic algorithms have represented the state of the art in manufacturing scheduling techniques, proving to be exceptionally reliable for optimising schedules. However, metaheuristics suffer from inherent weaknesses that inhibit their ability to be applied to dynamic cloud manufacturing (CMfg) scheduling problems in practice. Thanks to the very recent and rapidly accelerating development in deep reinforcement learning (DRL), a small sample of studies have described how those approaches have thoroughly outperformed metaheuristic algorithms in dynamic manufacturing scheduling problems, establishing a new state of the art. However, a significant lag in maturity exists between the algorithms used in CMfg and state-of-the-art DRL. This paper systematically reviews the CMfg scheduling literature published between 2010 and 2020, summarises the development of deep reinforcement learning in this context and offers valuable directions for future research.
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Ravn, Uffe Graaskov, Thomas Löhning, and Jesper Warschow Sørensen. "The First Step Towards BIM Models in Major Bridge Design." In IABSE Congress, Nanjing 2022: Bridges and Structures: Connection, Integration and Harmonisation. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2022. http://dx.doi.org/10.2749/nanjing.2022.0894.

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<p>The design of the world record 1915 Çanakkale Bridge is an example of the first steps towards using a BIM for a major bridge design. The model developed by COWI has been used to create 2D drawings and bar bending schedules, to manage interfaces and to check constructability, as well as health and safety related checks for working in congested space. Furthermore, it has proved to be an effective tool in communication with the project's stakeholders.</p><p>This paper describes where BIM has been applied in the design of the 1915 Çanakkale Bridge. Here special focus is on the construction of the plinth of the tower foundations where the contractor prefabricated the reinforcement cage for each plinth onshore, including all cast-in items and the steel form work and then lifted the whole assembly (18m in diameter) into place off-shore using a floating crane.</p>
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Parker, Jonathan, and Jeff Henry. "The Performance of Creep-Strengthened Ferritic Steels in Power Generating Plant." In ASME 2007 Pressure Vessels and Piping Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/creep2007-26368.

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Creep-strengthened ferritic steels, such as Grade 91, offer the potential for excellent high-temperature performance. To realize the benefits for these alloys requires careful control of original composition and manufacturing processes, such as welding and bending, as well as the associated heat treatments. Laboratory tests indicate that long-term lives may be below the original estimates made based on Larson Miller extrapolation. Furthermore, accelerated rates of damage accumulation in in-service Grade 91 components can occur due to a number of factors including: • Problems associated with design, for example with reinforcement at nozzles and with stress concentrations in piping systems. • Incorrect heat treatment, in addition to proper instrumentation appropriate heat treatment schedules should consider specific compositions. • Bending, problems may be introduced following both hot or cold bending. • High-temperature operation in tubing leading to excessive scale formation and overheating. • Type IV cracking in welds which results from the local reduction in the heat affected zone strength resulting from welding thermal cycles. Review of key information regarding the high-temperature performance of creep strengthened ferritic steels shows that the long-term creep strength may not achieve the levels expected from simple extrapolation of short term data. The problems experienced are highlighted with reference to actual case histories. The additional challenges associated with the development of creep-fatigue damage in high-temperature plant operated in a cyclic mode are also discussed.
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Koves, William, Elmar Upitis, Richard Cullotta, and Omar Latif. "Establishing Allowable Nozzle Loads." In ASME 2011 Pressure Vessels and Piping Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/pvp2011-57100.

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Every engineering project involving the design of pressure equipment, including pressure vessels, heat exchangers and the interconnecting piping requires that the interface loads between the equipment and piping be established for the pressure vessel nozzle design and the limitations on piping end reactions. The vessel or exchanger designer needs to know the external applied loads on nozzles and the piping designer needs to know the limiting end reactions on any connected equipment. However, the final loads are not known until the piping design is completed. This requires a very good estimate of the piping end loads prior to completing the vessel or piping design. The challenge is to develop a method of determining the optimum set of design loads prior to design. If the design loads are too low, the piping design may become too costly or impractical. If the design loads are too high the vessel nozzle designs will require unnecessary reinforcement and increased cost. The problem of the stresses at a nozzle to vessel intersection due to internal pressure and external forces and moments is one of the most complex problems in pressure vessel design. The problem has been studied extensively; however each study has its own limitations. Numerous analytical and numerical simulations have been performed providing guidance with associated limitations. The objective is to establish allowable nozzle load tables for the piping designer and the vessel designer. The loads and load combinations must be based on a technically accepted methodology and applicable to all nozzle sizes, pressure classes, schedules and vessel diameters and thicknesses and reinforcement designs within the scope of the tables. The internal design pressure must also be included along with the 3 forces and 3 moments that may be acting on the nozzle and the nozzle load tables must be adaptable to all materials of construction. The Tables must also be applicable for vessel heads. This paper presents the issues, including the limitations of some of the existing industry approaches, presents an approach to the problem, utilizing systematic Finite Element Analysis (FEA) methods and presents the results in the form of tables of allowable nozzle loads.
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Sahin, Taylan, Mate Boban, Ramin Khalili, and Adam Wolisz. "iVRLS: In-coverage Vehicular Reinforcement Learning Scheduler." In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, 2021. http://dx.doi.org/10.1109/vtc2021-spring51267.2021.9448993.

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Baheri, Betis, Jacob Tronge, Bo Fang, Ang Li, Vipin Chaudhary, and Qiang Guan. "MARS: Malleable Actor-Critic Reinforcement Learning Scheduler." In 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 2022. http://dx.doi.org/10.1109/ipccc55026.2022.9894315.

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Zhenbo Cheng, Ming Liang, and Zhidong Deng. "Optimal strategy for concurrent variable interval reinforcement schedule." In 2010 Chinese Control and Decision Conference (CCDC). IEEE, 2010. http://dx.doi.org/10.1109/ccdc.2010.5498938.

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