Academic literature on the topic 'Opponent model'
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Journal articles on the topic "Opponent model":
Davies, Ian, Zheng Tian, and Jun Wang. "Learning to Model Opponent Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Shen, Macheng, and Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.
Li, Junkang, Bruno Zanuttini, and Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (March 24, 2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.
Otto, Jacob, and William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action." International Studies Quarterly 65, no. 2 (November 19, 2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.
Wang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang, and Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability." Applied Sciences 12, no. 14 (July 8, 2022): 6953. http://dx.doi.org/10.3390/app12146953.
Liu, Chanjuan, Jinmiao Cong, Tianhao Zhao, and Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling." Mathematics 11, no. 14 (July 11, 2023): 3062. http://dx.doi.org/10.3390/math11143062.
Donkers, H. "Probabilistic opponent-model search." Information Sciences 135, no. 3-4 (July 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Redden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall, and Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems." Games 12, no. 3 (July 31, 2021): 62. http://dx.doi.org/10.3390/g12030062.
Donkers, H. "Admissibility in opponent-model search." Information Sciences 154, no. 3-4 (September 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Park, Hyunsoo, and 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.
Dissertations / Theses on the topic "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.
Koerper, 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.
Mojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)." Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Dissertation (MA)--University of Pretoria, 1993.
African Languages
unrestricted
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.
Li, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
In 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.
Title 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.
Books on the topic "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.
Graziano, William G., and Renée M. Tobin. Agreeableness and the Five Factor Model. Edited by Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.
Laver, Michael, and Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.
Hoppe, Sherry, and Bruce W. Speck, eds. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Thompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Thompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Laver, Michael, and Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Garloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Markwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Pick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Book chapters on the topic "Opponent model":
Donkers, Jeroen, Jaap van den Herik, and 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.
Chang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin, and 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.
van der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers, and 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.
van 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.
Donkers, H. H. L. M., H. J. Herik, and 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.
Salam, Khan Md Mahbubush, and 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.
Black, Elizabeth, and 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.
Bullock, Daniel, José L. Contreras-Vidal, and 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.
Zhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl, and 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.
Mudgal, Chhaya, and 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.
Conference papers on the topic "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.
Hernandez, Daniel, Hendrik Baier, and 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.
Ahumada, 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.
Gershon, Ron, and 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.
Zhang, Weinan, Xihuai Wang, Jian Shen, and 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.
Tian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, and 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.
Howett, 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.
Riley, Patrick, and 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.
Zafari, Farhad, and 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.
Mathibela, Bonolo, Ingmar Posner, and 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.
Reports on the topic "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.
Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Millán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, December 1999. http://dx.doi.org/10.18235/0006812.