Literatura científica selecionada sobre o tema "Opponent model"
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Artigos de revistas sobre o assunto "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.º 10 (3 de abril de 2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Texto completo da fonteShen, 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 de maio de 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.
Texto completo da fonteLi, Junkang, Bruno Zanuttini e Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 9 (24 de março de 2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.
Texto completo da fonteOtto, Jacob, e William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action". International Studies Quarterly 65, n.º 2 (19 de novembro de 2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.
Texto completo da fonteWang, 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.º 14 (8 de julho de 2022): 6953. http://dx.doi.org/10.3390/app12146953.
Texto completo da fonteLiu, Chanjuan, Jinmiao Cong, Tianhao Zhao e Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling". Mathematics 11, n.º 14 (11 de julho de 2023): 3062. http://dx.doi.org/10.3390/math11143062.
Texto completo da fonteDonkers, H. "Probabilistic opponent-model search". Information Sciences 135, n.º 3-4 (julho de 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Texto completo da fonteRedden, 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.º 3 (31 de julho de 2021): 62. http://dx.doi.org/10.3390/g12030062.
Texto completo da fonteDonkers, H. "Admissibility in opponent-model search". Information Sciences 154, n.º 3-4 (setembro de 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Texto completo da fontePark, 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.
Texto completo da fonteTeses / dissertações sobre o assunto "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 completo da fonteKoerper, 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 completo da fonteMojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)". Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Texto completo da fonteDissertation (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 completo da fonteLi, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
Texto completo da fonteIn 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 completo da fonteTitle 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.
Livros sobre o assunto "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.
Encontre o texto completo da fonteGraziano, William G., e 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 completo da fonteLaver, 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.
Texto completo da fonteHoppe, Sherry, e Bruce W. Speck, eds. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Texto completo da fonteThompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Texto completo da fonteThompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Texto completo da fonteLaver, Michael, e Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Texto completo da fonteGarloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Texto completo da fonteMarkwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Texto completo da fontePick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "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.
Texto completo da fonteChang, 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.
Texto completo da fontevan 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.
Texto completo da fontevan 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.
Texto completo da fonteDonkers, 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.
Texto completo da fonteSalam, 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.
Texto completo da fonteBlack, 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.
Texto completo da fonteBullock, 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.
Texto completo da fonteZhang, 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.
Texto completo da fonteMudgal, 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "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.
Texto completo da fonteHernandez, 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.
Texto completo da fonteAhumada, 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.
Texto completo da fonteGershon, 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.
Texto completo da fonteZhang, 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.
Texto completo da fonteTian, 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.
Texto completo da fonteHowett, 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.
Texto completo da fonteRiley, 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.
Texto completo da fonteZafari, 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.
Texto completo da fonteMathibela, 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "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 completo da fonteBobashev, Georgiy, John Holloway, Eric Solano e Boris Gutkin. A Control Theory Model of Smoking. RTI Press, junho de 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Texto completo da fonteMillán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, dezembro de 1999. http://dx.doi.org/10.18235/0006812.
Texto completo da fonte