Gotowa bibliografia na temat „Opponent model”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Opponent model”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Opponent model"
Davies, Ian, Zheng Tian i Jun Wang. "Learning to Model Opponent Learning (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Pełny tekst źródłaShen, Macheng, i Jonathan P. How. "Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 31 (17.05.2021): 578–87. http://dx.doi.org/10.1609/icaps.v31i1.16006.
Pełny tekst źródłaLi, Junkang, Bruno Zanuttini i Véronique Ventos. "Opponent-Model Search in Games with Incomplete Information". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 9 (24.03.2024): 9840–47. http://dx.doi.org/10.1609/aaai.v38i9.28844.
Pełny tekst źródłaOtto, Jacob, i William Spaniel. "Doubling Down: The Danger of Disclosing Secret Action". International Studies Quarterly 65, nr 2 (19.11.2020): 500–511. http://dx.doi.org/10.1093/isq/sqaa081.
Pełny tekst źródłaWang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang i Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability". Applied Sciences 12, nr 14 (8.07.2022): 6953. http://dx.doi.org/10.3390/app12146953.
Pełny tekst źródłaLiu, Chanjuan, Jinmiao Cong, Tianhao Zhao i Enqiang Zhu. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling". Mathematics 11, nr 14 (11.07.2023): 3062. http://dx.doi.org/10.3390/math11143062.
Pełny tekst źródłaDonkers, H. "Probabilistic opponent-model search". Information Sciences 135, nr 3-4 (lipiec 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Pełny tekst źródłaRedden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall i Olave E. Krigolson. "Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems". Games 12, nr 3 (31.07.2021): 62. http://dx.doi.org/10.3390/g12030062.
Pełny tekst źródłaDonkers, H. "Admissibility in opponent-model search". Information Sciences 154, nr 3-4 (wrzesień 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Pełny tekst źródłaPark, Hyunsoo, i 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.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaKoerper, 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.
Pełny tekst źródłaMojalefa, M. J. (Mawatle Jeremiah) 1948. "Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)". Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Pełny tekst źródłaDissertation (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.
Pełny tekst źródłaLi, Junkang. "Games with incomplete information : complexity, algorithmics, reasoning". Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
Pełny tekst źródłaIn 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.
Pełny tekst źródłaTitle 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.
Książki na temat "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.
Znajdź pełny tekst źródłaGraziano, William G., i Renée M. Tobin. Agreeableness and the Five Factor Model. Redaktor Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.
Pełny tekst źródłaLaver, Michael, i Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.
Pełny tekst źródłaHoppe, Sherry, i Bruce W. Speck, red. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Pełny tekst źródłaThompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Pełny tekst źródłaThompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Pełny tekst źródłaLaver, Michael, i Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Pełny tekst źródłaGarloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Pełny tekst źródłaMarkwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Pełny tekst źródłaPick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Pełny tekst źródłaCzęści książek na temat "Opponent model"
Donkers, Jeroen, Jaap van den Herik i Jos Uiterwijk. "Probabilistic Opponent-Model Search in Bao". W Entertainment Computing – ICEC 2004, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28643-1_53.
Pełny tekst źródłaChang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin i Tsan-sheng Hsu. "Opponent Model Selection Using Deep Learning". W Lecture Notes in Computer Science, 176–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11488-5_16.
Pełny tekst źródłavan der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers i Bob van der Vecht. "An Agent-Based Model for Emergent Opponent Behavior". W Lecture Notes in Computer Science, 290–303. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22741-8_21.
Pełny tekst źródłavan Galen Last, Niels. "Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation". W 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.
Pełny tekst źródłaDonkers, H. H. L. M., H. J. Herik i J. W. H. M. Uiterwijk. "Opponent-Model Search in Bao: Conditions for a Successful Application". W Advances in Computer Games, 309–24. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-387-35706-5_20.
Pełny tekst źródłaSalam, Khan Md Mahbubush, i Kazuyuki Ikko Takahashi. "Mathematical model of conflict and cooperation with non-annihilating multi-opponent". W 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.
Pełny tekst źródłaBlack, Elizabeth, i Anthony Hunter. "Reasons and Options for Updating an Opponent Model in Persuasion Dialogues". W 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.
Pełny tekst źródłaBullock, Daniel, José L. Contreras-Vidal i Stephen Grossberg. "Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control". W Neural Networks in Robotics, 439–57. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3180-7_25.
Pełny tekst źródłaZhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl i Shigang Yue. "O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night". W Lecture Notes in Computer Science, 249–60. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_21.
Pełny tekst źródłaMudgal, Chhaya, i Julita Vassileva. "Bilateral Negotiation with Incomplete and Uncertain Information: A Decision-Theoretic Approach Using a Model of the Opponent". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Opponent model"
Nakano, Yasuhisa. "New model for brightness perception". W Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fd5.
Pełny tekst źródłaHernandez, Daniel, Hendrik Baier i Michael Kaisers. "BRExIt: On Opponent Modelling in Expert Iteration". W 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.
Pełny tekst źródłaAhumada, Albert J. "Learning a red–green opponent system from LGN inputs". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.wr5.
Pełny tekst źródłaGershon, Ron, i John K. Tsotsos. "Experiments with a spatiochromatic model of early vision". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd2.
Pełny tekst źródłaZhang, Weinan, Xihuai Wang, Jian Shen i Ming Zhou. "Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts". W 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.
Pełny tekst źródłaTian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou i Jun Wang. "A Regularized Opponent Model with Maximum Entropy Objective". W 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.
Pełny tekst źródłaHowett, Gerald L. "Linear opponent-colors model optimized for brightness prediction". W OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wu3.
Pełny tekst źródłaRiley, Patrick, i Manuela Veloso. "Coaching a simulated soccer team by opponent model recognition". W the fifth international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375735.376034.
Pełny tekst źródłaZafari, Farhad, i Faria Nassiri-Mofakham. "POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)". W 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.
Pełny tekst źródłaMathibela, Bonolo, Ingmar Posner i Paul Newman. "A roadwork scene signature based on the opponent colour model". W 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696987.
Pełny tekst źródłaRaporty organizacyjne na temat "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.
Pełny tekst źródłaBobashev, Georgiy, John Holloway, Eric Solano i Boris Gutkin. A Control Theory Model of Smoking. RTI Press, czerwiec 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Pełny tekst źródłaMillán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, grudzień 1999. http://dx.doi.org/10.18235/0006812.
Pełny tekst źródła