Literatura académica sobre el tema "Opponent model"
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Artículos de revistas sobre el tema "Opponent model"
Davies, Ian, Zheng Tian y Jun Wang. "Learning to Model Opponent Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Texto completoShen, Macheng y Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17 de mayo de 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.
Texto completoLi, Junkang, Bruno Zanuttini y Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 9 (24 de marzo de 2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.
Texto completoOtto, Jacob y William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action". International Studies Quarterly 65, n.º 2 (19 de noviembre de 2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.
Texto completoWang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang y Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability". Applied Sciences 12, n.º 14 (8 de julio de 2022): 6953. http://dx.doi.org/10.3390/app12146953.
Texto completoLiu, Chanjuan, Jinmiao Cong, Tianhao Zhao y Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling". Mathematics 11, n.º 14 (11 de julio de 2023): 3062. http://dx.doi.org/10.3390/math11143062.
Texto completoDonkers, H. "Probabilistic opponent-model search". Information Sciences 135, n.º 3-4 (julio de 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Texto completoRedden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall y Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems". Games 12, n.º 3 (31 de julio de 2021): 62. http://dx.doi.org/10.3390/g12030062.
Texto completoDonkers, H. "Admissibility in opponent-model search". Information Sciences 154, n.º 3-4 (septiembre de 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Texto completoPark, Hyunsoo y Kyung-Joong Kim. "Active Player Modeling in the Iterated Prisoner’s Dilemma". Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7420984.
Texto completoTesis sobre el tema "Opponent model"
Lau, Hoi Ying. "Neural inspired color constancy model based on double opponent neurons /". View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20LAU.
Texto completoKoerper, Jason A. "A new colour quality model for ultra-high efficiency light sources with discontinuous spectra". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112359/1/Jason_Koerper_Thesis.pdf.
Texto completoMojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)". Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Texto completoDissertation (MA)--University of Pretoria, 1993.
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Sailer, Zbyněk. "Vyhledání podobných obrázků pomocí popisu barevným histogramem". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236514.
Texto completoLi, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
Texto completoIn this dissertation, we study games with incomplete information. We begin by establishing a complete landscape of the complexity of computing optimal pure strategies for different subclasses of games, when games are given explicitly as input. We then study the complexity when games are represented compactly (e.g.\ by their game rules). For this, we design two formalisms for such compact representations. Then we concentrate on games with incomplete information, by first proposing a new formalism called combinatorial game with incomplete information, which encompasses games of no chance (apart from a random initial drawing) and with only public actions. For such games, this new formalism captures the notion of information and knowledge of the players in a game better than extensive form. Next, we study algorithms and their optimisations for solving combinatorial games with incomplete information; some of these algorithms are applicable beyond these games. In the last part, we present a work in progress that concerns the modelling of recursive reasoning and different types of knowledge about the behaviour of the opponents in games with incomplete information
Hladky, Stephen Michael. "Predicting opponent locations in first-person shooter video games". Master's thesis, 2009. http://hdl.handle.net/10048/600.
Texto completoTitle from PDF file main screen (viewed on Oct. 2, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.
Libros sobre el tema "Opponent model"
K, Davis Paul. Thinking about opponent behavior in crisis and conflict: A generic model for analysis and group discussion. Santa Monica, CA: Rand, 1991.
Buscar texto completoGraziano, William G. y Renée M. Tobin. Agreeableness and the Five Factor Model. Editado por Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.
Texto completoLaver, Michael y Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.
Texto completoHoppe, Sherry y Bruce W. Speck, eds. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Texto completoThompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Texto completoThompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Texto completoLaver, Michael y Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Texto completoGarloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Texto completoMarkwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Texto completoPick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Texto completoCapítulos de libros sobre el tema "Opponent model"
Donkers, Jeroen, Jaap van den Herik y Jos Uiterwijk. "Probabilistic Opponent-Model Search in Bao". En Entertainment Computing – ICEC 2004, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_53.
Texto completoChang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin y Tsan-sheng Hsu. "Opponent Model Selection Using Deep Learning". En Lecture Notes in Computer Science, 176–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11488-5_16.
Texto completovan der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers y Bob van der Vecht. "An Agent-Based Model for Emergent Opponent Behavior". En Lecture Notes in Computer Science, 290–303. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22741-8_21.
Texto completovan Galen Last, Niels. "Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation". En New Trends in Agent-Based Complex Automated Negotiations, 167–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24696-8_12.
Texto completoDonkers, H. H. L. M., H. J. Herik y J. W. H. M. Uiterwijk. "Opponent-Model Search in Bao: Conditions for a Successful Application". En Advances in Computer Games, 309–24. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-35706-5_20.
Texto completoSalam, Khan Md Mahbubush y Kazuyuki Ikko Takahashi. "Mathematical model of conflict and cooperation with non-annihilating multi-opponent". En Unifying Themes in Complex Systems, 299–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-85081-6_38.
Texto completoBlack, Elizabeth y Anthony Hunter. "Reasons and Options for Updating an Opponent Model in Persuasion Dialogues". En Theory and Applications of Formal Argumentation, 21–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28460-6_2.
Texto completoBullock, Daniel, José L. Contreras-Vidal y Stephen Grossberg. "Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control". En Neural Networks in Robotics, 439–57. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_25.
Texto completoZhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl y Shigang Yue. "O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night". En Lecture Notes in Computer Science, 249–60. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_21.
Texto completoMudgal, Chhaya y Julita Vassileva. "Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent". En Cooperative Information Agents IV - The Future of Information Agents in Cyberspace, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-45012-2_11.
Texto completoActas de conferencias sobre el tema "Opponent model"
Nakano, Yasuhisa. "New model for brightness perception". En Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fd5.
Texto completoHernandez, Daniel, Hendrik Baier y Michael Kaisers. "BRExIt: On Opponent Modelling in Expert Iteration". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/422.
Texto completoAhumada, Albert J. "Learning a red–green opponent system from LGN inputs". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wr5.
Texto completoGershon, Ron y John K. Tsotsos. "Experiments with a spatiochromatic model of early vision". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd2.
Texto completoZhang, Weinan, Xihuai Wang, Jian Shen y Ming Zhou. "Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts". En 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.2021/466.
Texto completoTian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou y Jun Wang. "A Regularized Opponent Model with Maximum Entropy Objective". En 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.2019/85.
Texto completoHowett, Gerald L. "Linear opponent-colors model optimized for brightness prediction". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wu3.
Texto completoRiley, Patrick y Manuela Veloso. "Coaching a simulated soccer team by opponent model recognition". En the fifth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375735.376034.
Texto completoZafari, Farhad y Faria Nassiri-Mofakham. "POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/730.
Texto completoMathibela, Bonolo, Ingmar Posner y Paul Newman. "A roadwork scene signature based on the opponent colour model". En 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696987.
Texto completoInformes sobre el tema "Opponent model"
Howett, Gerald L. Linear opponent-colors model optimized for brightness prediction. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3202.
Texto completoBobashev, Georgiy, John Holloway, Eric Solano y Boris Gutkin. A Control Theory Model of Smoking. RTI Press, junio de 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Texto completoMillán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, diciembre de 1999. http://dx.doi.org/10.18235/0006812.
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