Готові списки джерел за темами / Counterfactual regret

Добірка наукової літератури з теми "Counterfactual regret"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Counterfactual regret"

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Summerville, Amy. "Counterfactual Seeking." Personality and Social Psychology Bulletin 37, no. 11 (July 6, 2011): 1522–33. http://dx.doi.org/10.1177/0146167211413295.

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Decision makers faced with an opportunity to learn the outcome of a foregone alternative must balance anticipated regret, should that information be unfavorable, with the potential benefits of this information in reducing experienced regret. Counterfactual seeking, the choice to learn more about foregone alternatives, may be a functional, regret-regulating strategy for individuals already experiencing regret. Counterfactual seeking increases in response to dissatisfying outcomes (Studies 1 and 2). Counterfactual seeking is generally able to reduce dissatisfaction (Study 2), regardless of whether individuals personally chose to view this information or were randomly assigned to do so (Study 3). Moreover, both imaginative (vs. factual) thoughts about the foregone option and upward (vs. downward) counterfactual thoughts play a role in this improvement in satisfaction (Study 4). Regret thus has a complex influence in how individuals engage with counterfactual information.
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2

Epstude, Kai, and Neal J. Roese. "When Goal Pursuit Fails." Social Psychology 42, no. 1 (January 2011): 19–27. http://dx.doi.org/10.1027/1864-9335/a000039.

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Counterfactual thoughts predominantly occur in response to failed goal pursuit. The primary function of self-related counterfactuals seems to be correction of specific behaviors and preparation for future successful goal attainment. In the present article we describe a model that outlines this view of counterfactual thoughts. We focus specifically on automatic versus controlled processes of counterfactual thinking and their relation to the formation of intentions. We link our model to research on goal pursuit, in which the impact of counterfactual thoughts and related affective experiences (e.g., regret) has been somewhat neglected. Implications for research on motivation and goal pursuit are discussed and novel predictions highlighted.
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Riddle Harding, Jennifer. "Evaluative stance and counterfactuals in language and literature." Language and Literature: International Journal of Stylistics 16, no. 3 (August 2007): 263–80. http://dx.doi.org/10.1177/0963947007079109.

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This article argues that speakers often express attitudes not only toward events that have happened, but also toward counterfactual events; speakers communicate these attitudes by expressing an evaluative stance toward counterfactual scenarios. By analyzing examples from a variety of discourse situations, from conversation to canonical literature, the author demonstrates that counterfactuals and evaluations function jointly to produce rhetorical effects. The options for expressing evaluative stance are described in detail, as are the four configurations of focal scenario and evaluative stance that may arise in discourse. By considering the connection between evaluative stance and emotion, the author explains the rhetorical connection between counterfactuals and feelings of relief and regret. With this theoretical and methodological framework established, the article then moves to consider the role of counterfactuals and evaluative stance in literature. In literature, different speakers, including characters and narrators, may imagine and describe counterfactuals, which are scenarios not realized in the story and which are regarded as unrealized by the speaker who introduces them. These narrative speakers, as well as the implied author, may also adopt an evaluative stance toward counterfactuals that are introduced. Readers must juggle these contending representations and evaluations. Because counterfactual scenarios are often depictions of foreclosed possibilities, lost opportunities, and near misses linked to strong feelings of relief and regret, they are evocative elements of narrative that reward readers for their mental work with an enhanced appreciation for characters and textual themes. Counterfactuals also encourage readers to take a participatory role in the process of judgment.
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4

Byrne, Ruth M. J. "Counterfactual Thinking: From Logic to Morality." Current Directions in Psychological Science 26, no. 4 (August 2017): 314–22. http://dx.doi.org/10.1177/0963721417695617.

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People create counterfactual alternatives to reality when they imagine how things would have turned out differently “if only. . . .” They understand counterfactuals by constructing models that correspond to the conjecture, and to the presupposed facts. The dual meaning of counterfactuals leads people to make more inferences from them compared to ordinary conditionals. People create counterfactuals by changing an aspect of reality that has been represented explicitly in their models, and they zoom in on a “fault line,” such as an exceptional event. However, knowledge can modulate what is represented in models and so the fault lines can shift. Counterfactuals permeate much of mental life, including judgments about morality, such as blame or praise, and assessments of emotions, such as regret or relief.
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Brown, Noam, and Tuomas Sandholm. "Solving Imperfect-Information Games via Discounted Regret Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1829–36. http://dx.doi.org/10.1609/aaai.v33i01.33011829.

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Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfectinformation games. In this paper we introduce novel CFR variants that 1) discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), 2) reweight iterations in various ways to obtain the output strategies, 3) use a non-standard regret minimizer and/or 4) leverage “optimistic regret matching”. They lead to dramatically improved performance in many settings. For one, we introduce a variant that outperforms CFR+, the prior state-of-the-art algorithm, in every game tested, including large-scale realistic settings. CFR+ is a formidable benchmark: no other algorithm has been able to outperform it. Finally, we show that, unlike CFR+, many of the important new variants are compatible with modern imperfect-informationgame pruning techniques and one is also compatible with sampling in the game tree.
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Coricelli, Giorgio, and Aldo Rustichini. "Counterfactual thinking and emotions: regret and envy learning." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1538 (January 27, 2010): 241–47. http://dx.doi.org/10.1098/rstb.2009.0159.

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Emotions like regret and envy share a common origin: they are motivated by the counterfactual thinking of what would have happened had we made a different choice. When we contemplate the outcome of a choice we made, we may use the information on the outcome of a choice we did not make. Regret is the purely private comparison between two choices that we could have taken, envy adds to this the information on outcome of choices of others. However, envy has a distinct social component, in that it adds the change in the social ranking that follows a difference in the outcomes. We study the theoretical foundation and the experimental test of this view.
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Hetts, John J., David S. Boninger, David A. Armor, Faith Gleicher, and Ariel Nathanson. "The influence of anticipated counterfactual regret on behavior." Psychology and Marketing 17, no. 4 (April 2000): 345–68. http://dx.doi.org/10.1002/(sici)1520-6793(200004)17:4<345::aid-mar5>3.0.co;2-m.

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Pierro, Antonio, Susanne Leder, Lucia Mannetti, E. Tory Higgins, Arie W. Kruglanski, and Antonio Aiello. "Regulatory mode effects on counterfactual thinking and regret." Journal of Experimental Social Psychology 44, no. 2 (March 2008): 321–29. http://dx.doi.org/10.1016/j.jesp.2007.06.002.

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Li, Huale, Xuan Wang, Fengwei Jia, Yulin Wu, Jiajia Zhang, and Shuhan Qi. "RLCFR: Minimize counterfactual regret by deep reinforcement learning." Expert Systems with Applications 187 (January 2022): 115953. http://dx.doi.org/10.1016/j.eswa.2021.115953.

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Nicolle, Antoinette, and Kevin Riggs. "The costs of giving up: Action versus inaction asymmetries in regret." Behavioral and Brain Sciences 36, no. 6 (December 2013): 702. http://dx.doi.org/10.1017/s0140525x13001143.

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AbstractKurzban et al.'s opportunity cost model of mental effort relies heavily on counterfactual thinking. We suggest that a closer inspection of the role of counterfactual emotions, and particularly of action/inaction asymmetries in anticipated regret, may be important in understanding the role of opportunity costs in decisions to persist with a current task.
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Дисертації з теми "Counterfactual regret"

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Fenderson, Carl R. "Regret, Relief, and Counterfactual Thinking: The Effect of Outcome Valence on Counterfactual Thinking." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1407510976.

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Putt, Caroline. "The effects of neurological damage on counterfactual thinking, regret, and decision making." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8083/.

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In 5 experiments neurologically damaged patients’ counterfactual reasoning, decision making (DM) and experience of counterfactually mediated emotions (CME) was explored. 17 patients, with varying lesion sites resulting from a stroke, (7 female) aged between 31 and 84 (M=64.5 years) and 17 controls (12 female) aged between 28 and 74 (M= 59 years) participated in the project. The project was conducted with three aims in mind; to establish if regret itself is experienced in brain damaged individuals; to explore the component processes of CMEs and widen the search for the brain areas that supports these; to conduct research that points to how the experience of regret directly impacts on future DM and underpins adaptive behavioural change. Through exploring the link between regret and adaptive choice switching, investigating counterfactual reasoning abilities, CME responses, emotional responses to The Regret Gambling Task (RGT) and responses in a task designed to measure risk taking, this project sheds new light on how neurological damage affects counterfactual reasoning, emotions and DM. In addition, two experiments were conducted with undergraduate students to explore whether CMEs are produced slowly, through deliberative processes, or quickly and effortlessly. Results were interpreted as evidence for fast and effortless CME production.
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McCoy, Mark Gordon. "The Dictator Game as a Test of the Social Affiliative Function of Counterfactual Expression." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu158550656004591.

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Beretta, Andrea. "Impact of counterfactual emotions on the experience of algorithm aversion." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/252452.

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Today more and more algorithms and their applications are entering into the everyday life of each of us. Algorithms can help people to make more effective choices through historical data analysis, generating predictions to present to the user in the form of advice and suggestions. Given the increasing popularity of these suggestions, a greater understanding of how people could increase their judgment through the suggestions presented is needed, in order to improve the interface design of these applications. Since the envision of Artificial Intelligence (AI), technical progress has the intent of surpassing human performance and abilities (Crandall et al., 2018). Less consideration has been given to improve cooperative relationships between human agents and computer agents during decision tasks. No study up to date has investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system. According to Zeelenberg et al. (Martinez & Zeelenberg, 2015; Martinez, Zeelenberg, & Rijsman, 2011a; Zeelenberg & Pieters, 1999), there are two emotions strongly related to wrong decisions, regret, and disappointment. The objective of this research is to understand the different effects of disappointment and regret on participants’ behavioral responses to failed suggestions given by algorithm-based systems. The research investigates how people deal with a computer suggestion that brings to a not satisfying result, compared to a human suggestion. To achieve this purpose, three different scenarios were tested in three different experiments. In the first experiment, the comparison was amongst two wrong suggestions in a between-subjects design through the presentation of a flight ticket scenario with two tasks. The first study analyzed exploratory models that explain the involvement of the source of suggestion and the trust in the systems in the experience of counterfactual emotions and responsibility attribution. The second experiment takes advantage of a typical purchase scenario, already used in the psychological literature, which had the aim to solve the issues found in the first study and test the algorithm aversion paradigm through the lenses of a classic study of regret literature. Results showed that, contrary to early predictions, people blame more the source of the suggestion when it comes from a human as compared with an intelligent computer suggestion. The third study had the aim to understand the role of counterfactuals through a paradigmatic experiment from algorithm aversion literature. In this study, the main finding is about the reliance people have on the algorithmic suggestion, which is higher compared to the reliance they have with a human suggestion. Nevertheless, people felt more guilt when they had a wrong outcome with a computer compared with a suggestion given by a person. Results are relevant in order to better understand how people decide and trust algorithm-based systems after a wrong outcome. This thesis is the first attempt to understand this algorithm aversion from the experienced counterfactual emotions and their different behavioral consequences. However, some of these findings showed contradictory results in the three experiments; this could be due to the different scenarios and participants’ thoughts and perceptions of artificial intelligence-based systems. From this work, three suggestions can be inferred to help designers of intelligent systems. The first regards the effective involvement of counterfactuals during the user interaction with a wrong outcome and the potential behavioral consequences that could affect the future use of the intelligent system. The second suggestion is the contribution to the importance of the context in which decisions are made, and the third guideline suggests the designer rethink about anthropomorphism as the best practice to present suggestions in the occurrence of potential wrong outcomes. Future works will investigate, in a more detailed way the perceptions of users and test different scenarios and decision domains.
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Туголукова, Євгенія Валеріївна. "Методи навчання з підкріпленням для гри в покер". Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/40598.

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Магістерська дисертація: 105 с., 16 рис., 20 табл., 1 додаток, 43 джерела. Об’єктом дослідження є ігри з неповною інформацією, на прикладі безлімітного техаського холдему для двох гравців. Предметом дослідження є методи навчання з підкріпленням для розв'язування ігор з неповною інформацією. Мета та цілі роботи – розглянути теоретичне підґрунтя ігор з неповною інформацією, провести дослідження існуючих методів їх розв'язування, розробка програмного забезпечення, яке вміє оптимально розв'язувати окремі ситуації в безлімітному техаському холдемі, та ігрові партії в цілому, аналіз розробленого програмного продукту. Дослідження ґрунтується на наукових публікаціях та інших матеріалах закордонних конференцій та архівів в галузі навчання з підкріпленням, глибокого навчання та пошуку виграшних стратегій в іграх. Результатом роботи є клієнт для гри у безлімітний техаський холдем, що автоматично може приймати рішення у ігрових ситуаціях.
Master's thesis: 105 pages, 16 figures, 20 tables, 1 appendixes, 43 sources. The object of the work are imperfect information games, on the example of No-limit Texas hold'em for two players. The subject of the work is reinforcement learning methods for solving games with incomplete information. The purpose and goals of the work – to consider the theoretical basis of games with incomplete information, to study existing methods of solving them, to develop software that can optimally solve individual situations in No-limit Texas Hold'em, and game in general, analysis of the developed software. The research is based on scientific publications and other materials of foreign conferences and archives in the field of reinforcement learning, deep learning and finding winning strategies in games. The result is a client for playing No-limit Texas Hold'em, which can automatically make decisions in game situations.
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Harding, Jennifer Riddle. "Simple regrets counterfactuals and the dialogic mind /." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1421.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: English Language and Literature. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Westfall, Jonathan E. "Exploring Common Antecedents of Three Related Decision Biases." Connect to full text in OhioLINK ETD Center, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1248468207.

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Trifunovic, Milica. "The principle of solidarity: A restatement of John Rawls' law of peoples." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2013. http://dx.doi.org/10.18452/16728.

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In der Dissertation habe ich versucht eine Theorie der globalen Gerechtigkeit darzustellen. Diese Theorie hat als ihre Basis das Denken von John Rawls. Rawls hat sich in seinem letzten Buch „Das Recht der Völker“ zu dem Thema geäußert. Ich erläutere seine Gedanken und zugleich kritisiere ich sie. Meine Kritikpunkte an Rawls: 1. Keine detaillierte Erläuterung der Aufteilung in ideale und nichtideale Theorie 2. Unvollkommenheit der Prinzipien für die ideale Theorie der globalen Gerechtigkeit Ich verweise auf die mögliche Verbesserung des Rawlschen Standpunktes über globale Gerechtigkeit durch: 1. eine Unterscheidung der normativen und deskriptiven idealen bzw. nichtidealen Theorie (damit wird die Struktur von Rawls‘s Arguments klarer) 2. Durch die Darstellung von drei Prinzipien die Rawlssche Prinzipien hätten sein könnten (damit bekommt seine Theorie inhaltlich mehr Wert) 3. Durch die Erläuterung des Prinzips der Solidarität als das, was an Rawls‘s Theorie besonders gewesen sein könnte.
In my disertation I have tried to present a theory of global justice. This theory has for its basis the thought of John Rawls. Rawls expressed his thaughts about the topic in his last book „The Law of Peoples.“ On the one hand I explain his theory while on the other I criticize it. My critique on Rawls goes accoring the following lines: 1. No elaborated disitiniction betwen the ideal and nonideal theory 2. Unfinished principles for the ideal theory of global justice I point to the possible amelioration of Rawls´ theory of global justice through: 1. Differentiation of the normative and descriptive ideal and nonideal theory (through this disticition is the stucture of Rawls´ argument clearer) 2. Introduction of three prinicples that should have been Rawls´s (through these three principles the content of his theory becomes more valuable) 3. Eplaning the principle of solidarity as the principle that could have been the specific for Rawls´ theory
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Reis, João dos Santos Rodrigues Soares dos. "A GPU implementation of Counterfactual Regret Minimization." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/83517.

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Marcos notáveis no avanço da Inteligência Artificial foram alcançados através da obtenção de soluções para jogos.A técnica da Minimização do Arrependimento tem sido muito usada no contexto da obtenção de soluções para jogos nos últimos anos. Em particular, Counterfactual Regret Minimization é um algoritmo que aplica esta técnica e pode ser usado para encontrar equilíbrios em jogos massivos. Portanto, pode-se usar um algoritmo deste género para desenvolver um agente com uma estratégia muito sólida para esses jogos.Um problema com este algoritmo é a quantidade de tempo de execução que exige, especialmente quando aplicada a jogos com enormes árvores de pesquisa. Para abordar este problema, os jogos são geralmente abstraídos o que pode levar a soluções piores.Esta dissertação propõe uma implementação do CFR que corre no GPU, usando CUDA, que é capaz de tirar partido da capacidade de GPUs para processar elevadas quantidades de dados de forma paralela. Usando esta abordagem, é possível reduzir o tempo de execução, como demonstram os resultados. Isto significa que este método tem o potencial de permitir o cálculo de Equilíbrios de Nash para jogos com um espaço de pesquisa maior do que antes.
Notable milestones in the advancement of Artificial Intelligence have been achieved through solving games. Regret minimization is a technique that has seen a lot of use in the context of solving games in the past few years. In particular, Counterfactual Regret Minimization (CFR) is an algorithm that applies this technique and can be used to find equilibria in massive games. Therefore, one can use an algorithm like this to develop an agent with a very solid strategy for such games.One issue with this algorithm is the amount of execution time it requires, especially when applied to large extensive games. To address this issue, games are usually abstracted which can lead to worse solutions.This dissertation proposes an implementation of CFR that runs on the GPU, using CUDA, which is able to take advantage of the ability of GPUs to process many parallel streams of data. Using this approach, it is possible to reduce the execution time in some Poker variants, as our results demonstrate. This means that this approach has the potential to allow the computation of Nash Equilibria for games with a larger search space than before.
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Velho, Nuno José da Rocha. "Implementação iterativa do algoritmo counterfactual regret minimization." Dissertação, 2014. https://repositorio-aberto.up.pt/handle/10216/74071.

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Книги з теми "Counterfactual regret"

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If only: How to turn regret into opportunity. New York: Broadway Books, 2005.

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Частини книг з теми "Counterfactual regret"

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Maitzen, Stephen. "The Counterfactual Past, Death, and Regret." In Determinism, Death, and Meaning, 121–59. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003229117-5.

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Neller, Todd W., and Steven Hnath. "Approximating Optimal Dudo Play with Fixed-Strategy Iteration Counterfactual Regret Minimization." In Lecture Notes in Computer Science, 170–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31866-5_15.

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Miller, Dale T., and Brian R. Taylor. "Counterfactual Thought, Regret, and Superstition: How to Avoid Kicking Yourself." In Heuristics and Biases, 367–78. Cambridge University Press, 2002. http://dx.doi.org/10.1017/cbo9780511808098.023.

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"Looking Back: From Metanoia to Buyer’s Regret." In Counterfactuals. Bloomsbury Academic, 2019. http://dx.doi.org/10.5040/9781350090118.ch-005.

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Harding, Jennifer Riddle. "Complex Regrets." In Similes, Puns, and Counterfactuals in Literary Narrative, 128–39. Routledge, 2017. http://dx.doi.org/10.4324/9781315682020-7.

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Тези доповідей конференцій з теми "Counterfactual regret"

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Zhang, Jie, Kefan Li, Baoming Zhang, Ming Xu, and Chongjun Wang. "Parallel Counterfactual Regret Minimization in Crowdsourcing Imperfect-information Expanded Game." In 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2021. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00195.

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Cotae, Paul, and Nii Emil Alexander Reindorf. "Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats." In 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE, 2021. http://dx.doi.org/10.1109/blackseacom52164.2021.9527857.

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Li, Shuxin, Youzhi Zhang, Xinrun Wang, Wanqi Xue, and Bo An. "CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/504.

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In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with individual strategies under the CFR framework, we transform the consistency relationship between strategies to the consistency relationship between the cumulative regret values. Furthermore, we propose a novel decomposition method over cumulative regret values to guarantee the consistency relationship between the cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which employs a mixing layer to estimate cumulative regret values of joint actions as a non-linear combination of cumulative regret values of individual actions. Experimental results show that CFR-MIX outperforms existing algorithms on various games significantly.
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Hu, Xiaoyan, Li Xia, Jun Yang, and Qianchuan Zhao. "A Fast-Convergence Method of Monte Carlo Counterfactual Regret Minimization for Imperfect Information Dynamic Games." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275075.

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Lockhart, Edward, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr TImbers, and Karl Tuyls. "Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/66.

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In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents. We prove that when following this optimization, the exploitability of a player's strategy converges asymptotically to zero, and hence when both players employ this optimization, the joint policies converge to a Nash equilibrium. Unlike fictitious play (XFP) and counterfactual regret minimization (CFR), our convergence result pertains to the policies being optimized rather than the average policies. Our experiments demonstrate convergence rates comparable to XFP and CFR in four benchmark games in the tabular case. Using function approximation, we find that our algorithm outperforms the tabular version in two of the games, which, to the best of our knowledge, is the first such result in imperfect information games among this class of algorithms.
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Čermák, Jiří, Viliam Lisý, and Branislav Bošanský. "Automated Construction of Bounded-Loss Imperfect-Recall Abstractions in Extensive-Form Games (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/701.

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Information abstraction is one of the methods for tackling large extensive-form games (EFGs). Removing some information available to players reduces the memory required for computing and storing strategies. We present novel domain-independent abstraction methods for creating very coarse abstractions of EFGs that still compute strategies that are (near) optimal in the original game. First, the methods start with an arbitrary abstraction of the original game (domain-specific or the coarsest possible). Next, they iteratively detect which information is required in the abstract game so that a (near) optimal strategy in the original game can be found and include this information into the abstract game. Moreover, the methods are able to exploit imperfect-recall abstractions where players can even forget the history of their own actions. We present two algorithms that follow these steps -- FPIRA, based on fictitious play, and CFR+IRA, based on counterfactual regret minimization. The experimental evaluation confirms that our methods can closely approximate Nash equilibrium of large games using abstraction with only 0.9% of information sets of the original game.
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Grujić, Tatjana. "L2 TENSE TRANSFER IN EFL LEARNING." In SCIENCE AND TEACHING IN EDUCATIONAL CONTEXT. FACULTY OF EDUCATION IN UŽICE, UNIVERSITY OF KRAGUJEVAC, 2020. http://dx.doi.org/10.46793/stec20.441g.

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In second language acquisition (SLA) transfer is predominantly explored as either positive or negative influence of learners’ first language (L1) on their second/foreign language (L2) performance. Studies in this field serve not only to describe the learner’s interlanguage, but also to inform, improve and refine foreign language teaching. However, the scope of SLA studies is such that it leaves the other transfer direction under-researched (L2 to L1), assuming that once the learner’s L1 system has fully developed, their L1 competence will not be subject to change. More recent studies of adult bilinguals have shown a bidirectional interaction between the two linguistic systems: not only does L1 influence L2, but L2 influences L1 as well. In this study, conducted among adult students of English (B2 to C1 level language users, according to CEFR), we examine the influence of English as a foreign language upon Serbian as a native tongue in terms of tense transfer. More precisely, the study explores how the subjects interpret and translate the secondary meanings of the English past tense. The basic meaning of the past tense is to locate an event (or state) in the past. However, in its secondary meanings (backshift past in reported clauses, counterfactual present in adverbial clauses of condition and ‘past subjunctive’ when expressing wishes and regrets) it does not refer to the past time. The error analysis of students’ English to Serbian translations provides evidence of L2 influence: learners tend to use the Serbian past rather than the present tense in their translations. Pedagogical implications of this study of misuse of L1 tense include focusing on explicit corrective feedback and polishing instructional materials.
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