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Journal articles on the topic 'Control action'

Author: Grafiati

Published: 5 June 2025

Last updated: 25 June 2025

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1

Vahidnia, Hamid, and Ronald Mitchell. "Employing Control in Entrepreneurial Actions: An Action-to-Action Model." Academy of Management Proceedings 2014, no. 1 (2014): 15046. http://dx.doi.org/10.5465/ambpp.2014.15046abstract.

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2

Kunde, Wilfried, Lisa Weller, and Roland Pfister. "Sociomotor action control." Psychonomic Bulletin & Review 25, no. 3 (2017): 917–31. http://dx.doi.org/10.3758/s13423-017-1316-6.

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3

Sotto, Richard. "“Spect-Action” technical control and organizational action." Studies in Cultures, Organizations and Societies 2, no. 1 (1996): 131–45. http://dx.doi.org/10.1080/10245289608523471.

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4

Hauf, Petra, and Gisa Aschersleben. "Action–effect anticipation in infant action control." Psychological Research 72, no. 2 (2006): 203–10. http://dx.doi.org/10.1007/s00426-006-0101-3.

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5

Callegari, João M. S., Lucas S. Araujo, Dener A. de L. Brandão, Braz J. Cardoso Filho, and Danilo I. Brandão. "Integrating Multiple Control Actions in Advanced Microgrids: an Analogy to Well-Established Inverter Control." Eletrônica de Potência 30 (February 6, 2025): e202516. https://doi.org/10.18618/rep.e202516.

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This paper proposes multiple control actions applied to centralized advanced microgrids (MGs). The well-established control actions feedback (FB), feedforward (FF), and disturbance decoupling (D) commonly used in current control of distributed energy resources (DERs) are herein extended to advanced MGs. Analytical expressions are derived to show the effect of each control action on the well-established DER current control and then on advanced MG control. Comprehensive comparisons between the feedback and all control action strategies are performed for both DER and MG controls. Simulation and experimental results show a dynamic response improvement in load disturbance rejection when all control actions are considered, as well as a reduction in steady-state error for the grid power flow control. By employing low-bandwidth communication between DERs and central controller, no hardware retrofit is required in comparison to what is needed in other state-of-the-art centralized MG controls.

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6

Dezfouli, Amir, Nura W. Lingawi, and Bernard W. Balleine. "Habits as action sequences: hierarchical action control and changes in outcome value." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1655 (2014): 20130482. http://dx.doi.org/10.1098/rstb.2013.0482.

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Goal-directed action involves making high-level choices that are implemented using previously acquired action sequences to attain desired goals. Such a hierarchical schema is necessary for goal-directed actions to be scalable to real-life situations, but results in decision-making that is less flexible than when action sequences are unfolded and the decision-maker deliberates step-by-step over the outcome of each individual action. In particular, from this perspective, the offline revaluation of any outcomes that fall within action sequence boundaries will be invisible to the high-level planner resulting in decisions that are insensitive to such changes. Here, within the context of a two-stage decision-making task, we demonstrate that this property can explain the emergence of habits. Next, we show how this hierarchical account explains the insensitivity of over-trained actions to changes in outcome value. Finally, we provide new data that show that, under extended extinction conditions, habitual behaviour can revert to goal-directed control, presumably as a consequence of decomposing action sequences into single actions. This hierarchical view suggests that the development of action sequences and the insensitivity of actions to changes in outcome value are essentially two sides of the same coin, explaining why these two aspects of automatic behaviour involve a shared neural structure.

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7

Mylopoulos, Myrto, and Elisabeth Pacherie. "Editorial: “Skilled Action Control”." Review of Philosophy and Psychology 12, no. 3 (2021): 469–80. http://dx.doi.org/10.1007/s13164-021-00563-4.

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8

Kray, Jutta, Rena Eenshuistra, Hannah Kerstner, Maaike Weidema, and Bernhard Hommel. "Language and Action Control." Psychological Science 17, no. 9 (2006): 737–41. http://dx.doi.org/10.1111/j.1467-9280.2006.01774.x.

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9

Jelenc, Marjetka, and Tit Albreht. "Joint Action Cancer Control." Slovenian Journal of Public Health 53, no. 3 (2014): 275–76. http://dx.doi.org/10.2478/sjph-2014-0029.

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10

Slack, J. M. W. "Growth control: Action mouse." Current Biology 7, no. 8 (1997): R467—R469. http://dx.doi.org/10.1016/s0960-9822(06)00238-7.

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11

Shepherd, Joshua. "Conscious Control over Action." Mind & Language 30, no. 3 (2015): 320–44. http://dx.doi.org/10.1111/mila.12082.

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12

Lobel, Samuel, Sreehari Rammohan, Bowen He, Shangqun Yu, and George Konidaris. "Q-functionals for Value-Based Continuous Control." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8932–39. http://dx.doi.org/10.1609/aaai.v37i7.26073.

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We present Q-functionals, an alternative architecture for continuous control deep reinforcement learning. Instead of returning a single value for a state-action pair, our network transforms a state into a function that can be rapidly evaluated in parallel for many actions, allowing us to efficiently choose high-value actions through sampling. This contrasts with the typical architecture of off-policy continuous control, where a policy network is trained for the sole purpose of selecting actions from the Q-function. We represent our action-dependent Q-function as a weighted sum of basis functions (Fourier, Polynomial, etc) over the action space, where the weights are state-dependent and output by the Q-functional network. Fast sampling makes practical a variety of techniques that require Monte-Carlo integration over Q-functions, and enables action-selection strategies besides simple value-maximization. We characterize our framework, describe various implementations of Q-functionals, and demonstrate strong performance on a suite of continuous control tasks.

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13

Hommel, Bernhard. "The role of action semantics in action control." Physics of Life Reviews 11, no. 2 (2014): 259–60. http://dx.doi.org/10.1016/j.plrev.2014.01.010.

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14

Hommel, Bernhard, and Reinout W. Wiers. "Towards a unitary approach to human action control." Trends in Cognitive Sciences 21, no. 12 (2017): 940–49. https://doi.org/10.1016/j.tics.2017.09.009.

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From its academic beginnings the theory of human action control has distinguished between endogenously driven, intentional action and exogenously driven, habitual, or automatic action. We challenge this dual-route model and argue that attempts to provide clear-cut and straightforward criteria to distinguish between intentional and automatic action have systematically failed. Specifically, we show that there is no evidence for intention-independent action, and that attempts to use the criterion of reward sensitivity and rationality to differentiate between intentional and automatic action are conceptually unsound. As a more parsimonious, and more feasible, alternative we suggest a unitary approach to action control, according to which actions are (i) represented by codes of their perceptual effects, (ii) selected by matching intention-sensitive selection criteria, and (ii) moderated by metacontrol states.

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15

Buckareff, Andrei A. "Mental Overpopulation and Mental Action: Protecting Intentions from Mental Birth Control." Canadian Journal of Philosophy 37, no. 1 (2007): 49–65. http://dx.doi.org/10.1353/cjp.2007.0009.

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Many, I suspect most, philosophers of action afford intentions a central role in theorizing about action and its explanation. Furthermore, current orthodoxy in the philosophy of action has it that intentions play a causal role with respect to the etiology and explanation of action. But action theory is not without its heretics. Some philosophers have challenged the orthodox view. In this paper I will examine and critique one such challenge. I will consider David-Hillel Ruben's case against the need for intentions to play a causal role in the etiology and explanation of mental actions. Contra Ruben, I will defend the orthodox view that intentions play an indispensable causal and explanatory role with respect to mental actions.

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16

Geurts, Dirk E. M., Quentin J. M. Huys, Hanneke E. M. den Ouden, and Roshan Cools. "Aversive Pavlovian Control of Instrumental Behavior in Humans." Journal of Cognitive Neuroscience 25, no. 9 (2013): 1428–41. http://dx.doi.org/10.1162/jocn_a_00425.

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Adaptive behavior involves interactions between systems regulating Pavlovian and instrumental control of actions. Here, we present the first investigation of the neural mechanisms underlying aversive Pavlovian–instrumental transfer using fMRI in humans. Recent evidence indicates that these Pavlovian influences on instrumental actions are action-specific: Instrumental approach is invigorated by appetitive Pavlovian cues but inhibited by aversive Pavlovian cues. Conversely, instrumental withdrawal is inhibited by appetitive Pavlovian cues but invigorated by aversive Pavlovian cues. We show that BOLD responses in the amygdala and the nucleus accumbens were associated with behavioral inhibition by aversive Pavlovian cues, irrespective of action context. Furthermore, BOLD responses in the ventromedial prefrontal cortex differed between approach and withdrawal actions. Aversive Pavlovian conditioned stimuli modulated connectivity between the ventromedial prefrontal cortex and the caudate nucleus. These results show that action-specific aversive control of instrumental behavior involves the modulation of fronto-striatal interactions by Pavlovian conditioned stimuli.

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17

Nattkemper, Dieter, and Michael Ziessler. "Cognitive control of action: The role of action effects." Psychological Research 68, no. 2-3 (2004): 71–73. http://dx.doi.org/10.1007/s00426-003-0145-6.

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18

Hauf, Petra, Birgit Elsner, and Gisa Aschersleben. "The role of action effects in infants? action control." Psychological Research 68, no. 2-3 (2004): 115–25. http://dx.doi.org/10.1007/s00426-003-0149-2.

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19

Ondobaka, Sasha, and Harold Bekkering. "Conceptual and perceptuo-motor action control and action recognition." Cortex 49, no. 10 (2013): 2966–67. http://dx.doi.org/10.1016/j.cortex.2013.06.005.

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20

Uithol, Sebo, Iris van Rooij, Harold Bekkering, and Pim Haselager. "Hierarchies in Action and Motor Control." Journal of Cognitive Neuroscience 24, no. 5 (2012): 1077–86. http://dx.doi.org/10.1162/jocn_a_00204.

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In analyses of the motor system, two hierarchies are often posited: The first—the action hierarchy—is a decomposition of an action into subactions and sub-subactions. The second—the control hierarchy—is a postulated hierarchy in the neural control processes that are supposed to bring about the action. A general assumption in cognitive neuroscience is that these two hierarchies are internally consistent and provide complementary descriptions of neuronal control processes. In this article, we suggest that neither offers a complete explanation and that they cannot be reconciled in a logical or conceptually coherent way. Furthermore, neither pays proper attention to the dynamics and temporal aspects of neural control processes. We will explore an alternative hierarchical organization in which causality is inherent in the dynamics over time. Specifically, high levels of the hierarchy encode more stable (goal-related) representations, whereas lower levels represent more transient (actions and motor acts) kinematics. If employed properly, a hierarchy based on this latter principle of temporal extension is not subject to the problems that plague the traditional accounts.

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21

Ivy, Spencer. "Unconscious Intelligence in the Skilled Control of Expert Action." Journal of Consciousness Studies 30, no. 3 (2023): 59–83. http://dx.doi.org/10.53765/20512201.30.3.059.

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What occurs in the mind of an expert who is performing at their very best? In this paper, I survey the history of debate concerning this question. I suggest that expertise is neither solely a mastery of the automatic nor solely a mastery of intelligence in skilled action control. Experts are also capable of performing automatic actions intelligently. Following this, I argue that unconscious-thought theory (UTT) is a powerful tool in coming to understand the role of executive, intelligent action control in the fluidly automatic performance of expertise. Relying on a body of empirical evidence concerning the cognitive structures and perceptual strategies employed by experts, I show that the realm of skilled action is an ideal environment within which the powers of unconscious cognitive processing can have positive effects on action. I conclude that experts rely upon unconscious thinking in the production of intelligently automatic skilled actions.

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22

Huestegge, Lynn, and Magali Kreutzfeldt. "Action effects in saccade control." Psychonomic Bulletin & Review 19, no. 2 (2012): 198–203. http://dx.doi.org/10.3758/s13423-011-0215-5.

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23

De Loo, I. "Management control bij action learning." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. 10 (2003): 445–52. http://dx.doi.org/10.5117/mab.77.11785.

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In tegenstelling tot de jaren zestig en zeventig van de vorige eeuw komt uit evaluaties van action learningprogramma’s steeds vaker naar voren dat zij weliswaar hebben geleid tot persoonlijke groei, maar niet langer tot organisatiegroei. In dit artikel wordt betoogd dat één van de belangrijkste oorzaken daarvan is dat er geen speci&#64257;eke rol voor management controlsystemen is weggelegd bij action learning. Wanneer echter vormen van ‘trial and error’ en ‘intuitive control’ zouden worden toegepast, is het niet ondenkbeeldig dat organisatiegroei wél weer gerealiseerd wordt, mits action learning wordt gebruikt in een context waarvoor het oorspronkelijk is ontwikkeld en er uitdrukkelijk rekening wordt gehouden met de taakspeci&#64257;citeit van de behandelde problemen.

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24

Elsner, Birgit, and Bernhard Hommel. "Effect anticipation and action control." Journal of Experimental Psychology: Human Perception and Performance 27, no. 1 (2001): 229–40. http://dx.doi.org/10.1037/0096-1523.27.1.229.

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25

Dickinson, Anthony, and Bernard Balleine. "Motivational Control of Instrumental Action." Current Directions in Psychological Science 4, no. 5 (1995): 162–67. http://dx.doi.org/10.1111/1467-8721.ep11512272.

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26

Coulthard, Elizabeth, Andrew Parton, and Masud Husain. "Action control in visual neglect." Neuropsychologia 44, no. 13 (2006): 2717–33. http://dx.doi.org/10.1016/j.neuropsychologia.2005.11.004.

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27

Jackson, Stephen R., and Masud Husain. "Visual control of hand action." Trends in Cognitive Sciences 1, no. 8 (1997): 310–17. http://dx.doi.org/10.1016/s1364-6613(97)01091-7.

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28

Amirav, Israel, and Michael T. Newhouse. "Asthma control and action plans." European Respiratory Journal 50, no. 6 (2017): 1701640. http://dx.doi.org/10.1183/13993003.01640-2017.

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29

Kouri, Andrew, Louis-Philippe Boulet, Alan Kaplan, and Samir Gupta. "Asthma control and action plans." European Respiratory Journal 50, no. 6 (2017): 1701883. http://dx.doi.org/10.1183/13993003.01883-2017.

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30

Hochstetter, Gregor, and Hong Yu Wong. "Comment: Affective Control of Action." Emotion Review 9, no. 4 (2017): 345–48. http://dx.doi.org/10.1177/1754073916684965.

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This commentary challenges Railton’s claim that the affective system is the key source of control of action. Whilst the affective system is important for understanding how acting for a reason is possible, we argue that there are many levels of control of action and adaptive behaviour and that the affective system is only one source of control. Such a model seems to be more in line with the emerging picture from affective and movement neuroscience.

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31

Nattkemper, Dieter, Michael Ziessler, and Peter A. Frensch. "Binding in voluntary action control." Neuroscience & Biobehavioral Reviews 34, no. 7 (2010): 1092–101. http://dx.doi.org/10.1016/j.neubiorev.2009.12.013.

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32

LAUWEREYNS, J. "Voluntary control of unavoidable action." Trends in Cognitive Sciences 10, no. 2 (2006): 47–49. http://dx.doi.org/10.1016/j.tics.2005.11.012.

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33

Barker, Steve, Clara Bertolissi, and Maribel Fernández. "Action Control by Term Rewriting." Electronic Notes in Theoretical Computer Science 234 (March 2009): 19–36. http://dx.doi.org/10.1016/j.entcs.2009.02.070.

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34

Sakata, Hideo, and Masato Taira. "Parietal control of hand action." Current Opinion in Neurobiology 4, no. 6 (1994): 847–56. http://dx.doi.org/10.1016/0959-4388(94)90133-3.

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35

De, Loo I. "Management control bij action learning." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. (10) (2003): 445–52. https://doi.org/10.5117/mab.77.11785.

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Abstract:

In tegenstelling tot de jaren zestig en zeventig van de vorige eeuw komt uit evaluaties van action learningprogramma's steeds vaker naar voren dat zij weliswaar hebben geleid tot persoonlijke groei, maar niet langer tot organisatiegroei. In dit artikel wordt betoogd dat één van de belangrijkste oorzaken daarvan is dat er geen specifieke rol voor management controlsystemen is weggelegd bij action learning. Wanneer echter vormen van 'trial and error' en 'intuitive control' zouden worden toegepast, is het niet ondenkbeeldig dat organisatiegroei wél weer gerealiseerd wordt, mits action learning wordt gebruikt in een context waarvoor het oorspronkelijk is ontwikkeld en er uitdrukkelijk rekening wordt gehouden met de taakspecificiteit van de behandelde problemen.

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36

Bramson, Bob, and Karin Roelofs. "Perceptual control or action-selection? Comment on: a perceptual control theory of emotional action." Cognition and Emotion 37, no. 7 (2023): 1193–98. http://dx.doi.org/10.1080/02699931.2023.2269830.

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37

Dezfouli, Amir, and Bernard W. Balleine. "Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized." PLoS Computational Biology 9, no. 12 (2013): e1003364. http://dx.doi.org/10.1371/journal.pcbi.1003364.

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38

Liu, Zhenxian, Peixi Peng, and Yonghong Tian. "Visual Reinforcement Learning with Residual Action." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19050–58. https://doi.org/10.1609/aaai.v39i18.34097.

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Learning control policy from continuous action space by visual observations is a fundamental and challenging task in reinforcement learning (RL). An essential problem is how to accurately map the high-dimensional images to the optimal actions by the policy network. Traditional decision-making modules output actions solely based on the current observation, while the distributions of optimal actions are dependent on specific tasks and cannot be known priorly, which increases the learning difficulty. To make the learning easier, we analyze the action characteristics in several control tasks, and propose Reinforcement Learning with Residual Action (ResAct) to explicitly model the adjustments of actions based on the differences between adjacent observations, rather than learning actions directly from observations. The method just redefines the output of the policy network, and doesn’t introduce any prior assumption to constrain or simplify the vanilla control problem. Extensive experiments on DeepMind Control Suite and CARLA demonstrate that the method could improve different RL baselines significantly, and achieve state-of-the-art performance.

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39

Liu, Qingsong, and Zhixuan Fang. "Learning the Optimal Control for Evolving Systems with Converging Dynamics." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 2 (2024): 1–39. http://dx.doi.org/10.1145/3656007.

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We consider a principle or controller that can pick actions from a fixed action set to control an evolving system with converging dynamics. The actions are interpreted as different configurations or policies. We consider systems with converging dynamics, i.e., if the principle holds the same action, the system will asymptotically converge (possibly requiring a significant amount of time) to a unique stable state determined by this action. This phenomenon can be observed in diverse domains such as epidemic control, computing systems, and markets. In our model, the dynamics of the system are unknown to the principle, and the principle can only receive bandit feedback (maybe noisy) on the impacts of his actions. The principle aims to learn which stable state yields the highest reward while adhering to specific constraints (i.e., optimal stable state) and to immerse the system into this state as quickly as possible. A unique challenge in our model is that the principle has no prior knowledge about the stable state of each action, but waits for the system to converge to the suboptimal stable states costs valuable time. We measure the principle's performance in terms of regret and constraint violation. In cases where the action set is finite, we propose a novel algorithm, termed Optimistic-Pessimistic Convergence and Confidence Bounds (OP-C2B), that knows to switch an action quickly if it is not worth waiting until the stable state is reached. This is enabled by employing "convergence bounds" to determine how far the system is from the stable states, and choosing actions through maintaining a pessimistic assessment of the set of feasible actions while acting optimistically within this set. We establish that OP-C2B can ensure sublinear regret and constraint violation simultaneously. Particularly, OP-C2B achieves logarithmic regret and constraint violation when the system convergence rate is linear or superlinear. Furthermore, we generalize our algorithm OP-C2B to the case of an infinite action set and demonstrate its ability to maintain sublinear regret and constraint violation. We finally show two game control problems including mobile crowdsensing and resource allocation that our model can address.

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40

Liu, Qingsong, and Zhixuan Fang. "Learning the Optimal Control for Evolving Systems with Converging Dynamics." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 29–30. http://dx.doi.org/10.1145/3673660.3655062.

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We consider a principle or controller that can pick actions from a fixed action set to control an evolving system with converging dynamics. The converging dynamics means that, if the principle holds the same action, the system will asymptotically converge to a unique stable state determined by this action. In our model, the dynamics of the system are unknown to the principle, and the principle can only receive bandit feedback (maybe noisy) on the impacts of his actions. The principle aims to learn which stable state yields the highest reward while adhering to specific constraints and to immerse the system into this state as quickly as possible. We measure the principle's performance in terms of regret and constraint violation. In cases where the action set is finite, we propose an algorithm Optimistic-Pessimistic Convergence and Confidence Bounds (OP-C2B) that ensures sublinear regret and constraint violation simultaneously. Particularly, OP-C2B achieves logarithmic regret and constraint violation when the system convergence rate is linear or superlinear. Furthermore, we generalize our algorithm OP-C2B to the case of an infinite action set and demonstrate its ability to maintain sublinear regret and constraint violation.

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41

Mukha, V. S., and N. F. Kako. "Dual Control of the Extremal Multidimensional Regression Object." Doklady BGUIR 20, no. 5 (2022): 21–30. http://dx.doi.org/10.35596/1729-7648-2022-20-5-21-30.

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The statement of the problem of the dual control of the regression object with multidimensional-matrix input and output variables and dynamic programming functional equations for its solution are given. The problem of the dual control of the extremal regression object, i.e. object response function of which has an extremum, is considered. The purpose of control is reaching the extremum of the output variable by sequential control actions in production operation mode. In order to solve the problem, the regression function of the object is supposed to be quadratic in input variables, and the inner noise is supposed to be Gaussian. The sequential solution of the functional dynamic programming equations is performed. As a result, the optimal control action at the last control step is obtained. It is shoved also that the optimal control actions obtaining at the other control steps is connected with big difficulties and impossible both analytically and numerically. The control action obtained at the last control step is proposed to be used at the arbitrary control step. This control action is called the control action with passive information accumulation. The dual control algorithm with passive information accumulation was programmed for numerical calculations and tested for a number of objects. It showed acceptable results for the practice.

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42

Press, Clare, Elena Gherri, Cecilia Heyes, and Martin Eimer. "Action Preparation Helps and Hinders Perception of Action." Journal of Cognitive Neuroscience 22, no. 10 (2010): 2198–211. http://dx.doi.org/10.1162/jocn.2009.21409.

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Several theories of the mechanisms linking perception and action require that the links are bidirectional, but there is a lack of consensus on the effects that action has on perception. We investigated this by measuring visual event-related brain potentials to observed hand actions while participants prepared responses that were spatially compatible (e.g., both were on the left side of the body) or incompatible and action type compatible (e.g., both were finger taps) or incompatible, with observed actions. An early enhanced processing of spatially compatible stimuli was observed, which is likely due to spatial attention. This was followed by an attenuation of processing for both spatially and action type compatible stimuli, likely to be driven by efference copy signals that attenuate processing of predicted sensory consequences of actions. Attenuation was not response-modality specific; it was found for manual stimuli when participants prepared manual and vocal responses, in line with the hypothesis that action control is hierarchically organized. These results indicate that spatial attention and forward model prediction mechanisms have opposite, but temporally distinct, effects on perception. This hypothesis can explain the inconsistency of recent findings on action–perception links and thereby supports the view that sensorimotor links are bidirectional. Such effects of action on perception are likely to be crucial, not only for the control of our own actions but also in sociocultural interaction, allowing us to predict the reactions of others to our own actions.

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43

Swirsky, Chloe L., Philip M. Fernbach, and Steven A. Sloman. "An illusion of control modulates the reluctance to tempt fate." Judgment and Decision Making 6, no. 7 (2011): 688–96. http://dx.doi.org/10.1017/s1930297500002692.

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AbstractThe tempting fate effect is that the probability of a fateful outcome is deemed higher following an action that “tempts” the outcome than in the absence of such an action. In this paper we evaluate the hypothesis that the effect is due to an illusion of control induced by a causal framing of the situation. Causal frames require that the action make a difference to an outcome and that the action precedes the outcome. If an illusion of control modulates the reluctance to tempt fate, then actions that make a difference to well-being and that occur prior to the outcome should tempt fate most strongly. In Experiments 1–3 we varied whether the action makes a difference and the temporal order of action and outcome. In Experiment 4 we tested whether an action can tempt fate if all outcomes are negative. The results of all four experiments supported our hypothesis that the tempting fate effect depends on a causal construal that gives rise to a false sense of control.

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44

Lee, Ganghun, Minji Kim, Minsu Lee, and Byoung-Tak Zhang. "Truncated Gaussian Policy for Debiased Continuous Control." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18071–81. https://doi.org/10.1609/aaai.v39i17.33988.

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In continuous domains, reinforcement learning policies are often based on Gaussian distributions for their generality. However, the unbounded support of Gaussian policy can cause a bias toward sampling boundary actions in many continuous control tasks that impose action limits due to physical constraints. This "boundary action bias'' can negatively impact training in algorithms like Proximal Policy Optimization. Despite this, it has been overlooked in many existing research and applications. In this paper, we revisit this issue by presenting illustrative explanations and analysis from the sampling point of view. Then, we introduce a truncated Gaussian policy with inherent bounds as a minimal alternative to mitigate the bias. However, we find that the plain truncated Gaussian policy may lay the counter-bias, preferring interior actions: to balance the bias, we ultimately propose a scale-adjusted truncated Gaussian policy, where the distribution scale shrinks if the location is near the boundaries. This property makes boundary actions deterministic more than in plain truncated Gaussian, but still less than in original Gaussian. Extensive empirical studies and comparisons on various continuous control tasks demonstrate that the truncated Gaussian policies significantly reduce the rate of boundary action usage, while scale-adjusted ones successfully balance the bias and counter-bias. It generally outperforms the Gaussian policy and shows competitive results compared to other approaches designed to counteract the bias.

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Ansari, Tahereh L., and Nazanin Derakshan. "Anxiety impairs inhibitory control but not volitional action control." Cognition & Emotion 24, no. 2 (2010): 241–54. http://dx.doi.org/10.1080/02699930903381531.

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Skinner, Ellen A. "Action, control judgments, and the structure of control experience." Psychological Review 92, no. 1 (1985): 39–58. http://dx.doi.org/10.1037/0033-295x.92.1.39.

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Crespo, Alfons, Ismael Ripoll, and Pedro Albertos. "Reducing Delays in RT Control: The Control Action Interval." IFAC Proceedings Volumes 32, no. 2 (1999): 8527–32. http://dx.doi.org/10.1016/s1474-6670(17)57454-6.

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Sebanz, Natalie, Günther Knoblich, Wolfgang Prinz, and Edmund Wascher. "Twin Peaks: An ERP Study of Action Planning and Control in Coacting Individuals." Journal of Cognitive Neuroscience 18, no. 5 (2006): 859–70. http://dx.doi.org/10.1162/jocn.2006.18.5.859.

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Previous studies have shown that perceiving another's actions activates corresponding representations in an observer's action system. The present study investigated how performing a task with another person affects action planning and control. Reaction times (RTs) and event-related potentials were measured while participants performed a go/no-go task alone and with another person. Three effects of acting together were observed. First, RTs were slowed when individuals had to respond to a stimulus referring to the other's action, suggesting that an action selection conflict occurred. Second, at frontal sites, a stimulus referring to the other's action elicited a similar electrophysiological response as a stimulus referring to one's own action. Finally, on no-go trials, P300 amplitude was significantly larger in a group setting, indicating that an action was suppressed. These findings provide evidence that individuals acting in a social context form shared action representations.

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GANESH, SUMITRA, and RUZENA BAJCSY. "LEARNING AND RECOGNITION OF HUMAN ACTIONS USING OPTIMAL CONTROL PRIMITIVES." International Journal of Humanoid Robotics 06, no. 03 (2009): 459–79. http://dx.doi.org/10.1142/s0219843609001802.

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We propose a unified approach for recognition and learning of human actions, based on an optimal control model of human motion. In this model, the goals and preferences of the agent engaged in a particular action are encapsulated as a cost function or performance criterion, that is optimized to yield the details of the movement. The cost function is a compact, intuitive and flexible representation of the action. A parameterized form of the cost function is considered, wherein the structure reflects the goals of the actions, and the parameters determine the relative weighting of different terms. We show how the cost function parameters can be estimated from data by solving a nonlinear least squares problem. The parameter estimation method is tested on motion capture data for two different reaching actions and six different subjects. We show that the problem of action recognition in the context of this representation is similar to that of mode estimation in a hybrid system and can be solved using a particle filter if a receding horizon formulation of the optimal controller is adopted. We use the proposed approach to recognize different reaching actions from the 3D hand trajectory of subjects.

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Frederick, Danny. "Free will and probability." Canadian Journal of Philosophy 43, no. 1 (2013): 60–77. http://dx.doi.org/10.1080/00455091.2013.803826.

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The chance objection to incompatibilist accounts of free action maintains that undetermined actions are not under the agent's control. Some attempts to circumvent this objection locate chance in eventsposteriorto the action. Indeterministic-causation theories locate chance in eventspriorto the action. However, neither type of response gives an account of free action which avoids the chance objection. Chance must be located at theact of willif actions are to be both undetermined and under the agent's control. This dissolves the apparent paradox of Frankfurt-type cases as well as the chance objection to incompatibilist free will.

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