Letteratura scientifica selezionata sul tema "Opponent model"
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Articoli di riviste sul tema "Opponent model"
Davies, Ian, Zheng Tian e Jun Wang. "Learning to Model Opponent Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 10 (3 aprile 2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Testo completoShen, Macheng, e Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17 maggio 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.
Testo completoLi, Junkang, Bruno Zanuttini e Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 9 (24 marzo 2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.
Testo completoOtto, Jacob, e William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action". International Studies Quarterly 65, n. 2 (19 novembre 2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.
Testo completoWang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang e Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability". Applied Sciences 12, n. 14 (8 luglio 2022): 6953. http://dx.doi.org/10.3390/app12146953.
Testo completoLiu, Chanjuan, Jinmiao Cong, Tianhao Zhao e Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling". Mathematics 11, n. 14 (11 luglio 2023): 3062. http://dx.doi.org/10.3390/math11143062.
Testo completoDonkers, H. "Probabilistic opponent-model search". Information Sciences 135, n. 3-4 (luglio 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Testo completoRedden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall e Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems". Games 12, n. 3 (31 luglio 2021): 62. http://dx.doi.org/10.3390/g12030062.
Testo completoDonkers, H. "Admissibility in opponent-model search". Information Sciences 154, n. 3-4 (settembre 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Testo completoPark, Hyunsoo, e 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.
Testo completoTesi sul 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.
Testo 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.
Testo completoMojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)". Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Testo 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.
Testo completoLi, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
Testo 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.
Testo 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.
Libri sul 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.
Cerca il testo completoGraziano, William G., e Renée M. Tobin. Agreeableness and the Five Factor Model. A cura di Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.
Testo completoLaver, Michael, e Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.
Testo completoHoppe, Sherry, e Bruce W. Speck, a cura di. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Testo completoThompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Testo completoThompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Testo completoLaver, Michael, e Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Testo completoGarloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Testo completoMarkwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Testo completoPick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Testo completoCapitoli di libri sul tema "Opponent model"
Donkers, Jeroen, Jaap van den Herik e Jos Uiterwijk. "Probabilistic Opponent-Model Search in Bao". In Entertainment Computing – ICEC 2004, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_53.
Testo completoChang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin e Tsan-sheng Hsu. "Opponent Model Selection Using Deep Learning". In Lecture Notes in Computer Science, 176–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11488-5_16.
Testo completovan der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers e Bob van der Vecht. "An Agent-Based Model for Emergent Opponent Behavior". In Lecture Notes in Computer Science, 290–303. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22741-8_21.
Testo completovan Galen Last, Niels. "Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation". In 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.
Testo completoDonkers, H. H. L. M., H. J. Herik e J. W. H. M. Uiterwijk. "Opponent-Model Search in Bao: Conditions for a Successful Application". In Advances in Computer Games, 309–24. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-35706-5_20.
Testo completoSalam, Khan Md Mahbubush, e Kazuyuki Ikko Takahashi. "Mathematical model of conflict and cooperation with non-annihilating multi-opponent". In 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.
Testo completoBlack, Elizabeth, e Anthony Hunter. "Reasons and Options for Updating an Opponent Model in Persuasion Dialogues". In 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.
Testo completoBullock, Daniel, José L. Contreras-Vidal e Stephen Grossberg. "Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control". In Neural Networks in Robotics, 439–57. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_25.
Testo completoZhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl e Shigang Yue. "O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night". In Lecture Notes in Computer Science, 249–60. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_21.
Testo completoMudgal, Chhaya, e Julita Vassileva. "Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent". In 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.
Testo completoAtti di convegni sul tema "Opponent model"
Nakano, Yasuhisa. "New model for brightness perception". In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fd5.
Testo completoHernandez, Daniel, Hendrik Baier e Michael Kaisers. "BRExIt: On Opponent Modelling in Expert Iteration". In 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.
Testo completoAhumada, Albert J. "Learning a red–green opponent system from LGN inputs". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wr5.
Testo completoGershon, Ron, e John K. Tsotsos. "Experiments with a spatiochromatic model of early vision". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd2.
Testo completoZhang, Weinan, Xihuai Wang, Jian Shen e Ming Zhou. "Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts". 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.2021/466.
Testo completoTian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou e Jun Wang. "A Regularized Opponent Model with Maximum Entropy Objective". 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.2019/85.
Testo completoHowett, Gerald L. "Linear opponent-colors model optimized for brightness prediction". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wu3.
Testo completoRiley, Patrick, e Manuela Veloso. "Coaching a simulated soccer team by opponent model recognition". In the fifth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375735.376034.
Testo completoZafari, Farhad, e Faria Nassiri-Mofakham. "POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)". In 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.
Testo completoMathibela, Bonolo, Ingmar Posner e Paul Newman. "A roadwork scene signature based on the opponent colour model". In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696987.
Testo completoRapporti di organizzazioni sul 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.
Testo completoBobashev, Georgiy, John Holloway, Eric Solano e Boris Gutkin. A Control Theory Model of Smoking. RTI Press, giugno 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Testo completoMillán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, dicembre 1999. http://dx.doi.org/10.18235/0006812.
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