Academic literature on the topic 'Approach-Avoidance Training'
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Journal articles on the topic "Approach-Avoidance Training"
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Van Dessel, Pieter, Sean Hughes, and Jan De Houwer. "Consequence-Based Approach-Avoidance Training: A New and Improved Method for Changing Behavior." Psychological Science 29, no. 12 (October 12, 2018): 1899–910. http://dx.doi.org/10.1177/0956797618796478.
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The repeated performance of approach or avoidance actions in response to specific stimuli (e.g., alcoholic drinks) is often considered a most promising type of cognitive-bias modification that can reduce unwanted behavior (e.g., alcohol consumption). Unfortunately, approach-avoidance training sometimes fails to produce desired outcomes (e.g., in the context of unhealthy eating). We introduce a novel training task in which approach-avoidance actions are followed by affective consequences. Four experiments (total N = 1,547) found stronger changes in voluntary approach-avoidance behavior, implicit and explicit evaluations, and consumer choices for consequence-based approach-avoidance training in the food domain. Moreover, this novel type of training reduced self-reported unhealthy eating behavior after a 24-hr delay and unhealthy snacking in a taste test. Our results contrast with dominant (association-formation) accounts of the effects of approach-avoidance training and support an inferential explanation. They further suggest that consequence-based approach-avoidance training, and inference training more generally, holds promise for the treatment of clinical behavior.
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Mertens, Gaëtan, Pieter Van Dessel, and Jan De Houwer. "The contextual malleability of approach-avoidance training effects: approaching or avoiding fear conditioned stimuli modulates effects of approach-avoidance training." Cognition and Emotion 32, no. 2 (March 27, 2017): 341–49. http://dx.doi.org/10.1080/02699931.2017.1308315.
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Machulska, Alla, Mike Rinck, Tim Klucken, Kristian Kleinke, Jana-Carina Wunder, Olga Remeniuk, and Jürgen Margraf. "“Push it!” or “Hold it!”? A comparison of nicotine-avoidance training and nicotine-inhibition training in smokers motivated to quit." Psychopharmacology 239, no. 1 (January 2022): 105–21. http://dx.doi.org/10.1007/s00213-021-06058-5.
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Abstract Rationale Recently, experimental paradigms have been developed to strengthen automatic avoidance or inhibitory responses for smoking cues. However, these procedures have not yet been directly compared regarding their effectiveness and mechanisms of action. Objective This study compared the effects of avoidance vs. inhibitory training as an add-on to a brief smoking cessation intervention. The standard Approach-Avoidance-Task (AAT) was adapted for both training types and control conditions. Methods One hundred twenty-four smokers attended behavioral counseling for smoking cessation and were thereafter randomized to one of four training conditions: avoidance-AAT, sham-avoidance-AAT, inhibition-AAT, sham-inhibition-AAT. During a 2-week training period including five training sessions, smokers in the avoidance-AAT trained to implicitly avoid all smoking-related cues, while smokers in the inhibition-AAT trained to implicitly inhibit behavioral response to smoking cues. During sham training, no such contingencies appeared. Self-report and behavioral data were assessed before and after training. Cigarette smoking and nicotine dependence were also assessed at 4- and 12-week follow-ups. Results At posttest, avoidance training was more effective in reducing daily smoking than inhibition training. However, this difference was no longer evident in follow-up assessments. All training conditions improved other smoking- and health-related outcomes. Neither training changed smoking-related approach biases or associations, but approach biases for smoking-unrelated pictures increased and Stroop interference decreased in all conditions. Smoking devaluation was also comparable in all groups. Conclusions Avoidance training might be slightly more effective in reducing smoking than inhibitory training. Overall, however, all four training types yielded equivalent therapy and training effects. Hence, a clear preference for one type of training remains premature.
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Woud, Marcella L., Eni S. Becker, Wolf-Gero Lange, and Mike Rinck. "Effects of Approach-Avoidance Training on Implicit and Explicit Evaluations of Neutral, Angry, and Smiling Face Stimuli." Psychological Reports 113, no. 1 (August 2013): 199–216. http://dx.doi.org/10.2466/21.07.pr0.113x10z1.
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A growing body of evidence shows that the prolonged execution of approach movements towards stimuli and avoidance movements away from them affects their evaluation. However, there has been no systematic investigation of such training effects. Therefore, the present study compared approach-avoidance training effects on various valenced representations of one neutral (Experiment 1, N = 85), angry (Experiment 2, N = 87), or smiling facial expressions (Experiment 3, N = 89). The face stimuli were shown on a computer screen, and by means of a joystick, participants pulled half of the faces closer (positive approach movement), and pushed the other half away (negative avoidance movement). Only implicit evaluations of neutral-expression were affected by the training procedure. The boundary conditions of such approach-avoidance training effects are discussed.
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Krishna, Anand, and Andreas B. Eder. "The influence of pre-training evaluative responses on approach-avoidance training outcomes." Cognition and Emotion 33, no. 7 (January 21, 2019): 1410–23. http://dx.doi.org/10.1080/02699931.2019.1568230.
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Van Dessel, Pieter, Jan De Houwer, Anne Gast, and Colin Tucker Smith. "Instruction-Based Approach-Avoidance Effects." Experimental Psychology 62, no. 3 (May 7, 2015): 161–69. http://dx.doi.org/10.1027/1618-3169/a000282.
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Prior research suggests that repeatedly approaching or avoiding a certain stimulus changes the liking of this stimulus. We investigated whether these effects of approach and avoidance training occur also when participants do not perform these actions but are merely instructed about the stimulus-action contingencies. Stimulus evaluations were registered using both implicit (Implicit Association Test and evaluative priming) and explicit measures (valence ratings). Instruction-based approach-avoidance effects were observed for relatively neutral fictitious social groups (i.e., Niffites and Luupites), but not for clearly valenced well-known social groups (i.e., Blacks and Whites). We conclude that instructions to approach or avoid stimuli can provide sufficient bases for establishing both implicit and explicit evaluations of novel stimuli and discuss several possible reasons for why similar instruction-based approach-avoidance effects were not found for valenced well-known stimuli.
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Li, Wenqian, Peiqiao Shang, and Jing Huang. "An Obstacle Avoidance Approach Based on Naive Bayes Classifier." Journal of Autonomous Intelligence 3, no. 1 (September 8, 2020): 27. http://dx.doi.org/10.32629/jai.v3i1.139.
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Obstacle avoidance plays an important role in mobile robot. However, the traditional methods of obstacle avoidance have difficulty in distinguishing multiple obstacles by edge detection. In this paper, the traditional obstacle avoidance methods are improved to realize the function of multi-obstacle avoidance. Regarding the implementation process, the LiDAR is used instead of the RGBD camera, which reduces the difficulty of handling image noise and achieves reliable obstacle detection. It can accurately detect the borders of the nearest obstacle even in complex environments and perform obstacle avoidance. Regarding the obstacle avoidance prediction, the model training is performed through the Naive Bayes classifier based on the three attributes of the velocity of the robot, the left boundary of the obstacle and the right boundary of the obstacle. In the training process, dataset was expanded to enhance the accuracy of classifier model. When the robot goes forward, the improved method enable the robot to move at a higher velocity. The results show the feasibility of advanced obstacle avoidance method by simulation.
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Van Dessel, Pieter, Andreas B. Eder, and Sean Hughes. "Mechanisms underlying effects of approach-avoidance training on stimulus evaluation." Journal of Experimental Psychology: Learning, Memory, and Cognition 44, no. 8 (August 2018): 1224–41. http://dx.doi.org/10.1037/xlm0000514.
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Kakoschke, Naomi, Rowan Page, Barbora de Courten, Antonio Verdejo-Garcia, and Jon McCormack. "Brain training with the body in mind: Towards gamified approach-avoidance training using virtual reality." International Journal of Human-Computer Studies 151 (July 2021): 102626. http://dx.doi.org/10.1016/j.ijhcs.2021.102626.
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Sun, Zhe, Yunsheng Fan, and Guofeng Wang. "An Intelligent Algorithm for USVs Collision Avoidance Based on Deep Reinforcement Learning Approach with Navigation Characteristics." Journal of Marine Science and Engineering 11, no. 4 (April 11, 2023): 812. http://dx.doi.org/10.3390/jmse11040812.
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Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience replay, noisy network, double learning, and dueling architecture, which can significantly enhance the training effect. Additionally, considering the characteristics of the USV collision avoidance problem, two effective methods to enhance training efficiency are proposed. For better training, considering the international regulations for preventing collisions at sea and USV maneuverability, a complete and reliable USV collision avoidance training system is established, demonstrating an efficient learning process in complex encounter situations. A reward signal system in line with the USV characteristics is designed. Based on the Unity maritime virtual simulation platform, an abundant simulation environment for training and testing is designed. Through detailed analysis, verification, and comparison, the improved algorithm outperforms the pre-improved algorithm in terms of stability, average reward, rules learning, and collision avoidance effect, reducing 26.60% more accumulated course deviation and saving 1.13% more time.
Dissertations / Theses on the topic "Approach-Avoidance Training"
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Kaczmarek, Nicolas. "Différentes voies pour favoriser le changement comportemental." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILH035.
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Plusieurs modèles soulignent l'importance de la motivation et de la volition pour favoriser le changement comportemental. Cette thèse a pour originalité de combiner certaines stratégies motivationnelles et/ou volitionnelles afin d'identifier les mécanismes générés et les potentiels modérateurs, et in fine, cerner comment maximiser leur efficacité. Cinq stratégies sont considérées : le contraste mental, le soutien à l'autodétermination de la motivation, le cadrage hiérarchique de buts (stratégies motivationnelles), l'entraînement à l'approche-évitement, et l'intention d'implémenter (stratégies volitionnelles). Le programme de recherche se décline en trois axes. L'axe 1 examine l'hypothèse selon laquelle une tâche difficile et stressante (Paced Auditory Serial Addition Test - Computerized) est mieux réussie, et génère plus d'autodétermination (Intrinsic Motivation Inventory) lorsque le but est cadré de manière hiérarchique (focalisation sur l'engagement) ou mixte hiérarchique-conditionnel (focalisation sur l'engagement et le résultat), que de manière conditionnelle (focalisation sur le résultat). Les résultats d'analyses fréquentistes des données de 167 étudiants (Mage = 23,3, SD = 3,7 ; 54,49% de femmes) ne confirment pas l'hypothèse. Le potentiel rôle du décalage entre cadrage de buts spontané et assigné, et d'autres facteurs motivationnels est discuté. L'axe 2 se focalise sur l'intérêt de combiner l'entraînement à l'approche-évitement et l'intention d'implémenter pour aider les patients souffrant de troubles de l'usage de l'alcool à changer une tendance automatique à approcher l'alcool. Des variantes de l'Approach-Avoidance Task sont réalisées dans trois conditions : les entraînements standard et avec intention d'implémenter impliquent d'éviter systématiquement l'alcool, seules les consignes différaient (« Je m'éloigne de l'alcool » vs. « Si je vois de l'alcool, alors je m'en éloigne ») ; la condition contrôle implique autant d'approcher que d'éviter l'alcool. L'hypothèse est que l'entraînement avec intention d'implémenter, et dans une moindre mesure l'entraînement standard (vs. condition contrôle), aident à changer : les tendances à l'approche-évitement (Approach-Avoidance Task) en post-test, et la consommation d'alcool (Alcohol Use Disorder Identification Test - Consumption), la dépendance (Short Alcohol Dependence Data questionnaire), et les retentissements biopsychosociaux (Short Inventory of Problems - Revised) en suivi à 2 mois. Nous avons validé et publié les questionnaires de dépendance et de retentissements en langue française. En août 2024, 75 patients souffrant de troubles de l'usage de l'alcool ont été recrutés (Mage = 47,5, SD = 11,6 ; 73,33% d'hommes) ; la collecte des données se poursuit actuellement. Les résultats préliminaires d'analyses bayésiennes fournissent des premiers éléments semblant souligner l'efficacité de l'entraînement mais l'absence de plus-value de l'intention d'implémenter. Des explications en termes de caractéristiques de l'intention d'implémenter utilisée et de dynamique de changement sont avancées. Le confirmatory report de cette étude est accepté à l'International Review of Social Psychology. L'axe 3 examine l'hypothèse d'une efficacité du contraste mental et de l'intention d'implémenter (vs. condition contrôle) pour favoriser l'activité physique (Godin Leisure Time Physical Activity Questionnaire) lors d'un suivi à 1 mois, qui serait augmentée en les combinant ensemble. Nous supposions également que le contraste mental agirait surtout sur l'engagement tandis que l'intention d'implémenter agirait surtout sur l'automaticité comportementale (Self-Report Behavioral Automaticity Index). Six cents adultes insuffisamment actifs seront recrutés. Les résultats d'analyses bayésiennes fourniront probablement des éléments de preuve pour statuer sur l'efficacité des stratégies et leur fonctionnement. Le confirmatory report de cette étude est accepté à l'International Review of Social PsychologySeveral models emphasize the importance of motivation and volition for promoting behavior change. The original aim of this thesis is to combine some motivational and/or volitional strategies to identify the generated mechanisms and potential moderators, and ultimately to determine how to maximize their effectiveness. Five strategies are considered: mental contrasting, motivational self-determination support, hierarchical goal framing (motivational strategies), approach-avoidance training, and implementation intentions (volitional strategies). The research program is divided into three axes. Axis 1 examines the hypothesis that a difficult and stressful task (Paced Auditory Serial Addition Test - Computerized) is better performed, and generates more self-determination (Intrinsic Motivation Inventory) when the goal is framed hierarchically (focus on commitment) or mixed hierarchically-conditionally (focus on commitment and outcome), than conditionally (focus on outcome). The results of frequentist analyses of data from 167 students (Mage = 23.3, SD = 3.7; 54.49% female) do not support the hypothesis. The potential role of the mismatch between spontaneous and assigned goal framing, as well as other motivational factors, is discussed. Axis 2 focuses on the interest of combining approach-avoidance training and implementation intentions to help patients with alcohol use disorders changing an automatic tendency to approach alcohol. Variants of the Approach-Avoidance Task were performed in three conditions: standard training and training with implementation intentions involved systematic avoidance of alcohol, with only the instructions differing (“I move away from alcohol” vs. “If I see alcohol, then I move away from it”); the control condition involves approaching and avoiding alcohol equally. The hypothesis is that training with implementation intentions, and to a lesser extent standard training (vs. control condition), helps to change: approach-avoidance tendencies (Approach-Avoidance Task) at post-test, and alcohol consumption (Alcohol Use Disorder Identification Test - Consumption), dependence (Short Alcohol Dependence Data questionnaire), and biopsychosocial repercussions (Short Inventory of Problems - Revised) at 2-month follow-up. We have validated and published the dependence and repercussions questionnaires in French. In August 2024, 75 patients with alcohol use disorders were recruited (Mage = 47.5, SD = 11.6; 73.33% male); data collection is currently ongoing. Preliminary results from Bayesian analyses provide initial evidence suggesting that training is effective, but that the implementation intentions has no added value. Explanations in terms of the characteristics of the implementation intentions used and the dynamics of change are put forward. A confirmatory report focusing on this study has been accepted by the International Review of Social Psychology. Axis 3 examined the hypothesis of an effectiveness of mental contrast and implementation intentions (vs. control condition) in promoting physical activity (Godin Leisure Time Physical Activity Questionnaire) at 1-month follow-up, and its enhancement by combining them together. It was also hypothesized that mental contrast would act primarily on engagement, while implementation intentions would act primarily on behavioral automaticity (Self-Report Behavioral Automaticity Index). Six hundred insufficiently active adults will be recruited. The results of Bayesian analyses are likely to provide evidence on the effectiveness of the strategies and how they work. A confirmatory report focusing on this study has been accepted by the International Review of Social Psychology
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Reibert, Evelyne [Verfasser], and Oliver [Akademischer Betreuer] Pogarell. "Evaluation des Approach-Avoidance Task Trainings als Zusatzintervention zu einer Standardbehandlung bei Tabakabhängigkeit / Evelyne Reibert ; Betreuer: Oliver Pogarell." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1211957624/34.
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Struwig, Gillian Anne. "Psychosocial factors and susceptibility to the common cold in distance runners." Thesis, 2004. http://hdl.handle.net/10500/1872.
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This study investigated the relationship between specific psychosocial factors and susceptibility to the common cold in a sample of 124 distance runners. A cross-sectional survey design was used to assess the role of life events, coping, hardiness, training workload and competition frequency in the athlete's risk of infection. Using correlational statistical techniques, it was found that the magnitude of recent life changes and the avoidance coping strategy of denial were positively related to self-reported symptoms of the common cold. Furthermore, a significant inverse correlation was observed between hardiness and symptom duration scores. However, approach coping, training workload and competition frequency were not significantly related to the dependent measures. The results of this study suggest that certain stress-related psychosocial factors are associated with susceptibility to the common cold in distance runners. Several strategies for the prevention and treatment of upper respiratory tract infections in this group are implied by these findings.Psychology
M.A. (Psychology)
Books on the topic "Approach-Avoidance Training"
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Office, General Accounting. Aviation Safety: Undeclared air shipments of dangerous goods and DOT's enforcement approach : report to the Ranking Minority Member, Subcommittee on Aviation, Committee on Transportation and Infrastructure, House of Representatives. Washington, D.C. (P.O. Box 37050, Washington 20013): U.S. General Accounting Office, 2003.
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Board, United States National Transportation Safety. Aircraft accident report: Flight into terrain during missed approach, USAir flight 1016, DC-9-31, N954VJ, Charlotte/Douglas International Airport, Charlotte, North Carolina, July 2, 1994. Washington, D.C: National Transportation Safety Board, 1995.
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Beninger, Richard J. Dopamine and the elements of incentive learning. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824091.003.0003.
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Dopamine and the elements of incentive learning explains how, in lever pressing for food tasks, incentive learning produces a gradient of attractiveness of environment stimuli: during magazine training, food activates dopaminergic neurons and the click and food cup become conditioned incentive stimuli, acquiring the ability to elicit approach and other responses; during lever-press training, the click activates dopaminergic neurons and the lever and lever-related stimuli become conditioned incentive stimuli. In conditioned place preference, amphetamine enhances dopaminergic neurotransmission and stimuli paired with amphetamine become conditioned incentive stimuli. In conditioned activity experiments, test-box stimuli paired with a dopamine-enhancer, e.g., cocaine, produce greater activity revealing incentive learning. In conditioned avoidance, the offset of an aversive warning stimulus putatively activates dopaminergic neurons leading safety-related stimuli to become conditioned incentive stimuli. If trained animals are treated with a dopamine receptor blocker, the initially intact ability of conditioned incentive stimuli to control responding declines over trials.
Book chapters on the topic "Approach-Avoidance Training"
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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science, 1–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.
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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.
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Yan, Liang, Xiaodong Wu, and Hangyu Lu. "Human-Centered Collaborative Decision-Making and Steering Control with Reinforcement Learning." In Lecture Notes in Mechanical Engineering, 742–48. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_105.
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AbstractThis paper presents a novel human-centered collaborative driving scheme using model-free reinforcement learning (RL) approach. The human-machine cooperation is achieved in both decision-making and steering control levels to improve driving safety while leaving space for human freedom as much as possible. A Markov decision process is firstly derived from the collaborative driving problem, then a RL agent is developed and trained to cooperatively control the vehicle steering under the guidance of a heuristic reward function. Twin delayed deep deterministic policy gradient (TD3) is conducted to attain the optimal control policy. In addition, two extended algorithms with distinct agent action definitions and training patterns are also devised. The effectiveness of the RL-based copilot system is finally validated in an obstacle avoidance scenario by simulation experiments. Driving performance and training efficiency of different RL agents are measured and compared to demonstrate the superiority of the proposed method.
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Silvia, Stephen J. "Nissan North America." In The UAW's Southern Gamble, 175–245. Cornell University Press, 2023. http://dx.doi.org/10.7591/cornell/9781501769696.003.0005.
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This chapter examines the organizing attempts at Nissan plants: two in Smyrna, Tennessee, in 1989 and 2001, and one in Canton, Mississippi, in 2017. There was also an organizing attempt at the Smyrna plant in 1997 that was substantial enough to count as a case but did not culminate with an election. The United Auto Workers' (UAW) approach to organizing changed with successive cases. Union officials learned from previous mistakes and integrated new tactics and technologies. Nissan managers also innovated. They pioneered a number of measures that subsequently became standard tactics in the union-avoidance playbook, including taking a team approach that minimized distinctions between management and line employees, cross-training employees to do multiple jobs, paying wages that were slightly lower than union contracts but markedly higher than the area rate, offering a wider range of benefits to line employees, avoiding layoffs whenever possible, using in-plant monitors to broadcast pro-management and anti-union messages, hiring a law firm that specializes in union avoidance, and cultivating close community relations. These measures proved effective in keeping the share of employees willing to vote for union representation well below a majority.
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Robinson, Selina W. M. "Applications of VR (Virtual Reality) Technology for Detection, Investigation, and Rehabilitation." In Cases on Forensic and Criminological Science for Criminal Detection and Avoidance, 313–37. IGI Global, 2024. http://dx.doi.org/10.4018/978-1-6684-9800-2.ch011.
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This chapter explores the extensive use of virtual reality (VR) technology within the UK's criminal justice system, focusing on its applications in investigation, correction, and rehabilitation. Driven by the need for efficient training solutions, VR is employed for crime scene reconstruction, inmate rehabilitation, and offender reintegration, as illustrated through case studies and scholarly literature. The chapter delves into the intersection of VR technology with various aspects of the criminal justice system, emphasizing its potential to improve investigative practices, enhance correctional outcomes, and support offender rehabilitation. It encompasses the technical capabilities of VR, its applications in criminal investigation, forensics, correctional facility management, and offender treatment programs, while also addressing ethical considerations and best practices for implementation. By harnessing the power of VR, the UK's criminal justice system can transform its approach to investigation, corrections, and rehabilitation.
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Ruskulis, Lilia, and Lidiia Aizikova. "SCIENTIFIC TEXT AS A MEANS FOR REALIZATION OF HIGHER EDUCATION INSTITUTION STUDENTS IN EDUCATIONAL AND SCIENTIFIC RESEARCH ACTIVITIES." In Trends of philological education development in the context of European integration. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-069-8-11.
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The article clarifies the peculiarities of the organization of educational and scientific research activity of higher education institution students, which is an important way to improve the quality of training and forming of specialists with an academic degree, able to creatively apply the latest achievements of scientific and technological progress, and opens opportunities for effective acquisition and use of knowledge; implements an individualized approach to learning; develops the ability to independently conduct research and summarize the investigation results; dominant tasks are characterized; the directions (educational research, scientific research and scientific organizational) and types of educational research activity of students (abstract, scientific reviews and articles, course and diploma papers) are analysed; the theoretical bases of stylistics and the concept of “style” are investigated; the substyles of the scientific style are analysed (proper scientific (academic, purely scientific); scientific and technical (production and technical); scientific humanitarian; scientific informative (scientific summarizing); scientific reference (reference-encyclopaedic). It is proved that the main purpose of scientific language is to create and produce the scientific text, by which we mean the highest communicative unit within scientific discourse, a holistic communicative block having a clear, logical structure and internally complete parts, saturated with relevant terminology, a set of constant text categories and a means of presenting scientific information, the results of scientific research. The levels of organization of the scientific text (linguistic-structural (operating with linguistic models); linguistic-cognitive (verbalized concepts in the text); communicative-rhetorical (means of persuasion in the scientific text); communicative-pragmatic (personal attitude of the one who produces a scientific text to the message) are investigated; the features of primary (monograph, dissertation, bachelor and master theses) and secondary (scientific article, abstracts, summaries, annotations, reviews, reports) scientific texts are characterized. The paper reveals the principles of compiling scientific texts: content saturation – innovativeness of the presented information, its cognitive value; professional core – the need for analysed information for a particular sphere; scientific informativeness – the author’s concept of the represented research; novelty of the scientific text – new observations and knowledge discoveries that can be implemented in practice; content completeness – the integrity of the presented statements; problematicity – coding of problematic issues; comprehensibility to a specialist in a particular field – apprehensibility of information and providing necessary conditions to understanding it; intertextuality – connection of the scientific text with other types of texts; text declarativeness – a clear comparative analysis of a particular process or phenomenon. The stages of work on the scientific text (organizational, research, generalization of research results) are studied. Requirements for the creation of scientific texts are defined: clear structure (division into chapters, sections, units, paragraphs and sentences that are closely related to each other), avoiding of repetitions (in particular, in conclusions to chapters and in final conclusions); deliberate use of graphic material; systemacity in the process of writing the text; avoidance of concepts that cannot be unambiguously interpreted; justified use of figures and facts; text coherence. The requirements to the structure of the scientific text (introduction, research part, conclusions) are covered.
Conference papers on the topic "Approach-Avoidance Training"
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Sajad, Allawati, and Saad Hiam. "Blended Approach to Stuck Pipe Avoidance Training." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216860-ms.
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Abstract The objectives of the project is to develop a blended training program for Stuck Pipe Avoidance using e-Learning, video learning and Virtual Reality, VR, to increase participants attention throughout the course and to enhance knowledge retention. PDO works in a complex, high-risk, high-consequence work environment where accidents can be extremely serious, causing damage to people, assets, and the environment. The traditional multiple day course content was reviewed and updated to incorporate VR technology into a blended training program for Stuck Pipe Avoidance with e-learning, 3D models, animation, and video learning modules so that the company could train their people in the safest way possible whilst providing a high level of realism and knowledge retention and allowing the trainees to learn more effectively and efficiently. Use-case studies and historical experience were incorporated into development of the new content. The vendor used the stage gate approach to get the SME's approval before developing the content into digital formats. A blended approach resulted in increased participants attention and gamified VR training modules helped participants to massively increase their knowledge retention by creating highly immersive virtual learning environments which helped them to focus all of their attention on their training. This creative approach helped the company to deliver training experiences that were truly immersive and engaging without the usual constraints, limitations and costs imposed by the physical world, allowing candidates to fail in a safe environment. Pre and post training questionnaires confirmed Class participation increased, employee motivation increased, and knowledge retention increased, throughout the new blended course compared to the traditional approach. VR is being increasingly used in education and training institutions around the world but has been slow to be adopted by the Oil & Gas industry. This project clearly demonstrates the benefits to include VR technology into a blended approach, being better prepared to mitigate risks in high risk, high consequences environments.
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Lindenberg, K., and C. Ebner. "Eine app-basiertes Approach-Avoidance-Task-Training zur Reduktion von Symptomen bei Jugendlichen mit riskantem Mediengebrauch." In Deutscher Suchtkongress 2022. Georg Thieme Verlag, 2022. http://dx.doi.org/10.1055/s-0042-1756019.
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McEnroe, Patrick, Shen Wang, and Madhusanka Liyanage. "Towards Faster DRL Training: An Edge AI Approach for UAV Obstacle Avoidance by Splitting Complex Environments." In 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). IEEE, 2024. http://dx.doi.org/10.1109/ccnc51664.2024.10454660.
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Soares, Natália Souza, João Marcelo Xavier Natário Teixeira, and Veronica Teichrieb. "Robot training in virtual environments using Reinforcement Learning techniques." In Anais Estendidos do Simpósio de Realidade Virtual e Aumentada. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/svr_estendido.2020.12950.
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In this work, we propose a framework to train a robot in a virtual environment using Reinforcement Learning (RL) techniques and thus facilitating the use of this type of approach in robotics. With our integrated solution for virtual training, it is possible to programmatically change the environment parameters, making it easy to implement domain randomization techniques on-the-fly. We conducted experiments with a TurtleBot 2i in an indoor navigation task with static obstacle avoidance using an RL algorithm called Proximal Policy Optimization (PPO). Our results show that even though the training did not use any real data, the trained model was able to generalize to different virtual environments and real-world scenes.
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Zhou, Qi, Sikai Li, Jingbo Qu, Jin Wu, Haomiao Xu, and Youyi Bi. "An Adaptive Path Planning Approach for Digital Twin-Enabled Robot Arm Based on Inverse Kinematics and Deep Reinforcement Learning." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113131.
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Abstract Efficient path planning methods for robot arms are crucial to ensure the quality and safety of their completing various tasks. Compared to traditional manual instruction, Reinforcement Learning (RL) based path planning methods show better adaptability for complex working scenarios. However, the training of RL is usually time-consuming with limited success rate. To tackle this problem, we propose an adaptive path planning approach for robot arm based on Inverse Kinematics (IK) and Deep Reinforcement Learning (DRL) in a pick-and-place context. A judgement mechanism is developed to adaptively select IK or RL based method according to the results of early-stage collision detection. We separate the pick and place task into three sequential curriculums (approaching, grabbing and placing) with modified reward functions to speed up the training process and achieve a higher success rate. The proposed approach is validated with a physical robot arm supported by a high-fidelity digital twin model. The experiment results show that our proposed approach outperforms traditional RL based method with improved training speed and guaranteed performance in collision avoidance and path accuracy. This work contributes to the practical deployment of RL based path planning method for digital twin-enabled robot arm in smart manufacturing.
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Wang, Xinrui, and Yan Jin. "Work Process Transfer Reinforcement Learning: Feature Extraction and Finetuning in Ship Collision Avoidance." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91145.
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Abstract The advancement of artificial intelligence and machine learning technologies has led to significant changes in work processes. The computer agents are applied to perform not only routine and repetitive jobs but also highly complex tasks such as driving a car and steering a ship. Given the sensory information of the environment, a reinforcement learning method has been applied for agents to learn how to perform complex tasks by trial and error through interactions with the environment. To overcome the issues such as limited and sparse training data, researchers are attempting to reuse the previously learned knowledge in new task situations. In this paper, we investigate how feature extraction and finetuning methods can be combined to allow computer agents to perform transfer reinforcement learning more effectively and efficiently in the context of ship collision avoidance. Taking a computer simulation-based empirical approach, we first develop a ship collision avoidance gameplay environment by introducing the own ship, target ships, and the base case and target cases. A deep neural network including four convolutional layers and three fully connected layers is devised for work process feature capturing through deep reinforcement learning. The case study results have shown that features do exist in work processes, and they can be captured and reused. The similarity between the source case and the target case is a key factor that determines how the feature extract and finetuning methods should be combined for effective task results and efficient learning processes.
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Noel, A., K. Shreyanka, K. G. S. Kumar, B. M. Shameem, and B. Akshar. "Autonomous Ship Navigation Methods: A Review." In International Conference on Marine Engineering and Technology Oman. London: IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.028.
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Autonomous navigation is achieved by training or programming the ship with the stored data about the vessel behavior in various sailing environment. The autonomous behaviour relies on intelligent analytics based on machine learning algorithms. As a major advance in machine learning, the deep learning approach is becoming a powerful technique for autonomy. The deep learning methodologies are applied in various fields in the maritime industry such as detecting anomalies, ship classification, collision avoidance, risk detection of cyber attacks, navigation in ports and so on. The present paper reviews on various methods available in the literature for vessel autonomy and their applications in ship navigation. The focus of the work is to illustrate the advantages of deep learning approach over the machine learning and other traditional methods.
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Chen, Haochong, and Bilin Aksun Guvenc. "Deep Reinforcement Learning Based Collision Avoidance of Automated Driving Agent." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2556.
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<div class="section abstract"><div class="htmlview paragraph">Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically. First, the A* path searching algorithm is applied to generate an optimal path from origin to destination for the agent represented by waypoints. Further, preview path tracking errors, steering control and distance to destination are introduced to build the reward function. In addition, raw data from multiple sensors is processed separately and concatenated together to help the proposed agent get a comprehensive understanding of its environment. Two traffic scenarios including traffic rule free urban road and road segment with two intersections, traffic light and stop sign are used to evaluate the performance of the proposed automated driving agent. The performance of proposed Deep Q-Learning (DQN) agent is evaluated in multiple aspects. Compared to traditional mid-to-mid DRL agent with explicit decomposition of high-level behavior decision and low-level control, the proposed DRL agents are expected to have better performance and smaller size since all processing steps are optimized simultaneously. Moreover, the pre-calculated A* path provides a good reference point for subsequent DRL training.</div></div>
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Kalra, Jay, Anjali Saxena, and Zoher Rafid-Hamed. "Medical Error Disclosure in Healthcare – The Scene across Canada." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004370.
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The quality of healthcare is an emerging concern worldwide. Despite the advancement in the medical field, adverse events resulting from medical errors are relatively common in healthcare systems. Disclosure of an adverse event is an important element in managing the consequences of a medical error. We have previously reviewed and compared various disclosure policies that are in practice in Canada and around the globe to analyze the progress made in this area and suggested a non-punitive, “no-fault” model for reporting medical errors. The purpose of this study was to review and compare the disclosure policies implemented by individual health authorities across the Canadian provinces and territories. We evaluated each policy based on the inclusion of the following key points: Apology, avoidance of blame, avoidance of speculation, immediate disclosure, patient support, provider support, provider training, team-based approach, accessibility, and documentation. The clinical significance of the study was to evaluate various health authorities’ policies of disclosure and report a practice model for medical error disclosure across Canada. The three top parameters found within the disclosure policies include an apology or expression of regret, a team-based approach and documentation of disclosure, all three averaging at 98% respectively across the provinces and territories. The bottom two parameters found within the disclosure policies include provider training and accessibility of disclosure policy through the health authorities’ website, both averaging at 34% respectively. We believe healthcare providers' top priority should be correcting flaws in the medical system and protecting patients' health. Despite the obstacles, physicians should seek to disclose medical errors to patients and their families on both ethical and pragmatic grounds. We believe that the disclosure policies can provide framework and guidelines for appropriate disclosure, which can lead to improved quality care and practices that are more transparent. We suggest that disclosure practice can be improved by creating a uniform policy, centered on honest disclosure and addressing errors in a non-punitive manner.
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Le, Trung, Hung Vu, Tu Dinh Nguyen, and Dinh Phung. "Geometric Enclosing Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/326.
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Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G\left(\bz\right) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data.