Results for ' reinforcement'

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  1.  37
    Reinforcement Learning and Counterfactual Reasoning Explain Adaptive Behavior in a Changing Environment.Yunfeng Zhang, Jaehyon Paik & Peter Pirolli - 2015 - Topics in Cognitive Science 7 (2):368-381.
    Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and (...)
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  2. Integrating reinforcement learning, bidding and genetic algorithms.Ron Sun - unknown
    This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by the Q module. (...)
     
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  3. Reinforcing the Three ‘R’s: Reduction, Reception, and Replacement.Ronald P. Endicott - 2007 - In M. Schouten & H. Looren de Jong (eds.), The Matter of the Mind: Philosophical Essays on Psychology, Neuroscience, and Reduction. Blackwell.
    Philosophers of science have offered different accounts of what it means for one scientific theory to reduce to another. I propose a more or less friendly amendment to Kenneth Schaffner’s “General Reduction-Replacement” model of scientific unification. Schaffner interprets scientific unification broadly in terms of a continuum from theory reduction to theory replacement. As such, his account leaves no place on its continuum for type irreducible and irreplaceable theories. The same is true for other accounts that incorporate Schaffner's continuum, for example, (...)
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  4. Partial-reinforcement eliminates the massed shock deficit.E. da WilliamsTinio - 1992 - Bulletin of the Psychonomic Society 30 (6):445-445.
     
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  5. Reinforcement learning.Chris Jch Watkins & Peter Dayan - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
  6.  30
    Secondary reinforcement in rats as a function of information value and reliability of the stimulus.M. David Egger & Neal E. Miller - 1962 - Journal of Experimental Psychology 64 (2):97.
  7. Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs.Yu Zhao, Jifeng Guo, Chengchao Bai & Hongxing Zheng - 2021 - Complexity 2021:1-12.
    A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve (...)
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  8.  93
    Automatic Reinforcement: An Important but Ignored Concept.Margaret E. Vaughan & Jack L. Michael - 1982 - Behaviorism 10 (2):217-227.
  9.  45
    Self-reinforcement: Theoretical and methodological considerations.Albert Bandura - 1976 - Behaviorism 4 (2):135-155.
  10. Reinforcing the knowledge account of assertion.Martijn Blaauw - 2012 - Analysis 72 (1):105-108.
    Many philosophers are building a solid case in favour of the knowledge account of assertion (KAA). According to KAA, if one asserts that P one represents oneself as knowing that P. KAA has recently received support from linguistic data about prompting challenges, parenthetical positioning and predictions. In this article, I add another argument to this rapidly growing list: an argument from what I will call ‘reinforcing parenthesis’.
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  11. Backward reinforcement and invariance in reinforcements to acquisition.Ba Williams - 1987 - Bulletin of the Psychonomic Society 25 (5):343-343.
  12. Representation-Reinforcing Review: Comparing Experiences in the United States and Australia.David Tucker - 2003 - In Tom Campbell, Jeffrey Goldsworthy & Adrienne Stone (eds.), Protecting Human Rights: Instruments and Institutions. Oxford University Press.
     
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  13.  16
    From reinforcement of acts to reinforcement of social preferences.Daniel John Zizzo - 2002 - Behavioral and Brain Sciences 25 (2):282-283.
    Rachlin rightly highlights behavioural reinforcement, conditional cooperation, and framing. However, genes may explain part of the variance in altruistic behaviour. Framing cannot be used to support his theory of altruism. Reinforcement of acts is not identical to reinforcement of patterns of acts. Further, many patterns of acts could be reinforced, and Rachlin's altruism is not the most likely candidate.
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  14.  27
    Reinforcement, expectancy, and learning.Robert C. Bolles - 1972 - Psychological Review 79 (5):394-409.
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  15.  47
    Deep Reinforcement Learning as Foundation for Artificial General Intelligence.Itamar Arel - 2012 - In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. pp. 89--102.
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  16.  37
    Integrating reinforcement learning with models of representation learning.Matt Jones & Fabián Canas - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1258--1263.
  17.  6
    Reinforcement Learning.Oliver Kramer - 2009 - In L. Magnani (ed.), Computational Intelligence. pp. 101--117.
  18. Reinforcement learning with raw image pixels as state input.D. Ernst, R. Marée & L. Wehenkel - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4153.
  19. Operant Reinforcement Theory and Determinism.Robert H. Vorsteg - 1974 - Behavior and Philosophy 2 (1):108.
  20.  9
    Reinforcing property of music for non-human animals: Analysis with pigeons.Shigeru Watanabe, Takako Suzuki & Yumiko Yamazaki - 2009 - In David Papineau (ed.), Philosophy. Oxford University Press. pp. 121--1.
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  21.  2
    Reinforcement learning in factories: the auton project.Andrew W. Moore - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. pp. 18--12.
  22. Reinforcement learning: A brief guide for philosophers of mind.Julia Haas - 2022 - Philosophy Compass 17 (9):e12865.
    I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several assumptions about the nature of reinforcement learning and its instantiation in minds like ours. I then review some of the contributions of reinforcement learning methods have made across the so-called 'decision sciences.' Finally, I show how principles from reinforcement learning can shape philosophical debates regarding the nature of perception and characterisations of desire.
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  23. Reinforced variability and creativity.A. Neuringer - 2003 - In Kennon A. Lattal (ed.), Behavior Theory and Philosophy. Springer.
     
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  24. Differential reinforcement expectancies and successive dmts performance in rats.Js Cohen & J. Douglas - 1986 - Bulletin of the Psychonomic Society 24 (5):321-321.
     
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  25. Self‐Reinforcing and Self‐Frustrating Decisions.Caspar Hare & Brian Hedden - 2016 - Noûs 50 (3):604-628.
  26. Multi-Agent Reinforcement Learning: Weighting and Partitioning.Ron Sun & Todd Peterson - unknown
    This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in (...) learning tasks, di erent ways of partitioning a task and using agents selectively based on partitioning. Based on the analysis, some heuristic methods are described and experimentally tested. We nd that some o -line heuristic methods performed the best, signi cantly better than single-agent models. (shrink)
     
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  27.  20
    Network formation by reinforcement learning: The long and medium run.Brian Skyrms - unknown
    We investigate a simple stochastic model of social network formation by the process of reinforcement learning with discounting of the past. In the limit, for any value of the discounting parameter, small, stable cliques are formed. However, the time it takes to reach the limiting state in which cliques have formed is very sensitive to the discounting parameter. Depending on this value, the limiting result may or may not be a good predictor for realistic observation times.
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  28.  53
    Reinforcing ethical decision making through organizational structure.Harvey S. James - 2000 - Journal of Business Ethics 28 (1):43 - 58.
    In this paper I examine how the constituent elements of a firm's organizational structure affect the ethical behavior of workers. The formal features of organizations I examine are the compensation practices, performance and evaluation systems, and decision-making assignments. I argue that the formal organizational structure, which is distinguished from corporate culture, is necessary, though not sufficient, in solving ethical problems within firms. At best the formal structure should not undermine the ethical actions of workers. When combined with a strong culture, (...)
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  29.  78
    Reinforcing ethical decision making through corporate culture.Al Y. S. Chen, Roby B. Sawyers & Paul F. Williams - 1997 - Journal of Business Ethics 16 (8):855-865.
    Behaving ethically depends on the ability to recognize that ethical issues exist, to see from an ethical point of view. This ability to see and respond ethically may be related more to attributes of corporate culture than to attributes of individual employees. Efforts to increase ethical standards and decrease pressure to behave unethically should therefore concentrate on the organization and its culture. The purpose of this paper is to discuss how total quality (TQ) techniques can facilitate the development of a (...)
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  30.  14
    Partial reinforcement: A hypothesis of sequential effects.E. J. Capaldi - 1966 - Psychological Review 73 (5):459-477.
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  31.  26
    Reinforcement learning: A brief guide for philosophers of mind.Julia Haas - 2022 - Philosophy Compass 17 (9):e12865.
    In this opinionated review, I draw attention to some of the contributions reinforcement learning can make to questions in the philosophy of mind. In particular, I highlight reinforcement learning's foundational emphasis on the role of reward in agent learning, and canvass two ways in which the framework may advance our understanding of perception and motivation.
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  32.  8
    A reinforcement model of imprinting: Implications for socialization in monkeys and men.Howard S. Hoffman & Alan M. Ratner - 1973 - Psychological Review 80 (6):527-544.
  33.  12
    Reinforcement and extinction as factors in size estimation.William W. Lambert, Richard L. Solomon & Peter D. Watson - 1949 - Journal of Experimental Psychology 39 (5):637.
  34.  11
    Stimulus-reinforcer predictiveness and selective discrimination learning in pigeons.Edward A. Wasserman - 1974 - Journal of Experimental Psychology 103 (2):284.
  35.  23
    Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.A. David Redish, Steve Jensen, Adam Johnson & Zeb Kurth-Nelson - 2007 - Psychological Review 114 (3):784-805.
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  36.  6
    Evolutionary Reinforcement Learning for Adaptively Detecting Database Intrusions.Seul-Gi Choi & Sung-Bae Cho - 2020 - Logic Journal of the IGPL 28 (4):449-460.
    Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. (...)
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  37.  17
    Can reinforcement theory account for avoidance?Benbow F. Ritchie - 1951 - Psychological Review 58 (5):382-386.
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  38.  17
    Partial reinforcement in a gambling situation.Donald J. Lewis - 1952 - Journal of Experimental Psychology 43 (6):447.
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  39.  8
    Reinforcement of leverholding by avoidance of shock.Hank Davis & Jo-Ann Burton - 1976 - Bulletin of the Psychonomic Society 8 (1):61-64.
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  40.  6
    Conditioned reinforcement and reproductive success.Edmund Fantino - 1988 - Behavioral and Brain Sciences 11 (1):135-135.
  41.  7
    Partial reinforcement before and after continuous reinforcement.John Theios & Rodney W. Mcginnis - 1967 - Journal of Experimental Psychology 73 (3):479.
  42.  7
    Reinforcement, explanation, and B. F. Skinner.Robert Epstein - 1978 - Behavioral and Brain Sciences 1 (1):57-58.
  43. Does Identity Politics Reinforce Oppression?Katherine Ritchie - forthcoming - Philosophers' Imprint.
    Identity politics has been critiqued in various ways. One central problem—the Reinforcement Problem—claims that identity politics reinforces groups rooted in oppression thereby undermining its own liberatory aims. Here I consider two versions of the problem—one psychological and one metaphysical. I defang the first by drawing on work in social psychology. I then argue that careful consideration of the metaphysics of social groups and of the practice of identity politics provides resources to dissolve the second version. Identity politics involves the (...)
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  44.  12
    Reinforcers and reinforcement: Their relation to maze performance.William Bevan & Robert Adamson - 1960 - Journal of Experimental Psychology 59 (4):226.
  45.  16
    Intermittent reinforcement, nonreversal shifts, and neutralizing in concept formation.Isidore Gormezano & Fred D. Abraham - 1961 - Journal of Experimental Psychology 61 (1):1.
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  46.  13
    The reinforcement relation as a function of instrumental response base rate.Robert W. Schaeffer - 1965 - Journal of Experimental Psychology 69 (4):419.
  47.  16
    Reinforcement schedules in habit reversal—a confirmation.Joseph H. Grosslight, John F. Hall & Winfield Scott - 1954 - Journal of Experimental Psychology 48 (3):173.
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  48.  25
    Reinforcing or Challenging Stigma? The Risks and Benefits of ‘Dignity Talk’ in Sex Work Discourse.Stewart Cunningham - 2016 - International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique 29 (1):45-65.
    The concept of ‘human dignity’ sits at the heart of international human rights law and a growing number of national constitutions and yet its meaning is heavily contested and contingent. I aim to supplement the theoretical literature on dignity by providing an empirical study of how the concept is used in the specific context of legal discourse on sex work. I will analyse jurisprudence in which commercial sex was declared as incompatible with human dignity, focussing on the South African Constitutional (...)
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  49. The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI.Samuel Allen Alexander - 2020 - Journal of Artificial General Intelligence 11 (1):70-85.
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two (...)
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  50.  19
    Partial reinforcement effects (PREs) and inverse PREs determined by position of a nonrewarded block of responses.Glen D. Jensen - 1961 - Journal of Experimental Psychology 62 (5):461.
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