Auswahl der wissenschaftlichen Literatur zum Thema „Opponent model“
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Zeitschriftenartikel zum Thema "Opponent model"
Davies, Ian, Zheng Tian und Jun Wang. „Learning to Model Opponent Learning (Student Abstract)“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 10 (03.04.2020): 13771–72. http://dx.doi.org/10.1609/aaai.v34i10.7157.
Der volle Inhalt der QuelleShen, Macheng, und 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.
Der volle Inhalt der QuelleLi, Junkang, Bruno Zanuttini und 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.
Der volle Inhalt der QuelleOtto, Jacob, und 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.
Der volle Inhalt der QuelleWang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang und Zhongjie Zhang. „Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability“. Applied Sciences 12, Nr. 14 (08.07.2022): 6953. http://dx.doi.org/10.3390/app12146953.
Der volle Inhalt der QuelleLiu, Chanjuan, Jinmiao Cong, Tianhao Zhao und 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.
Der volle Inhalt der QuelleDonkers, H. „Probabilistic opponent-model search“. Information Sciences 135, Nr. 3-4 (Juli 2001): 123–49. http://dx.doi.org/10.1016/s0020-0255(01)00133-5.
Der volle Inhalt der QuelleRedden, Ralph S., Greg A. Gagliardi, Chad C. Williams, Cameron D. Hassall und 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.
Der volle Inhalt der QuelleDonkers, H. „Admissibility in opponent-model search“. Information Sciences 154, Nr. 3-4 (September 2003): 119–40. http://dx.doi.org/10.1016/s0020-0255(03)00046-x.
Der volle Inhalt der QuellePark, Hyunsoo, und 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.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleKoerper, 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.
Der volle Inhalt der QuelleMojalefa, M. J. (Mawatle Jeremiah) 1948. „Tshekatsheko ya Sebilwane bjalo ka thetokanegelo (Sepedi)“. Diss., University of Pretoria, 1993. http://hdl.handle.net/2263/24295.
Der volle Inhalt der QuelleDissertation (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.
Der volle Inhalt der QuelleLi, Junkang. „Games with incomplete information : complexity, algorithmics, reasoning“. Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMC270.
Der volle Inhalt der QuelleIn 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.
Der volle Inhalt der QuelleTitle 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.
Bücher zum Thema "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.
Den vollen Inhalt der Quelle findenGraziano, William G., und Renée M. Tobin. Agreeableness and the Five Factor Model. Herausgegeben von Thomas A. Widiger. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.17.
Der volle Inhalt der QuelleLaver, Michael, und Ernest Sergenti. Endogenous Parties, Interaction of Different Decision Rules. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0006.
Der volle Inhalt der QuelleHoppe, Sherry, und Bruce W. Speck, Hrsg. Service-Learning. Praeger, 2004. http://dx.doi.org/10.5040/9798216013013.
Der volle Inhalt der QuelleThompson, Douglas I. Montaigne and the Tolerance of Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.001.0001.
Der volle Inhalt der QuelleThompson, Douglas I. The Power of Uncivil Conversation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190679934.003.0004.
Der volle Inhalt der QuelleLaver, Michael, und Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.
Der volle Inhalt der QuelleGarloff, Katja. Mixed Feelings. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501704963.001.0001.
Der volle Inhalt der QuelleMarkwica, Robin. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794349.003.0001.
Der volle Inhalt der QuellePick, Daniel. 1. Introduction. Oxford University Press, 2015. http://dx.doi.org/10.1093/actrade/9780199226818.003.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Opponent model"
Donkers, Jeroen, Jaap van den Herik und 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.
Der volle Inhalt der QuelleChang, Hung-Jui, Cheng Yueh, Gang-Yu Fan, Ting-Yu Lin und 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.
Der volle Inhalt der Quellevan der Zwet, Koen, Ana Isabel Barros, Tom M. van Engers und 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.
Der volle Inhalt der Quellevan 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.
Der volle Inhalt der QuelleDonkers, H. H. L. M., H. J. Herik und 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.
Der volle Inhalt der QuelleSalam, Khan Md Mahbubush, und 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.
Der volle Inhalt der QuelleBlack, Elizabeth, und 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.
Der volle Inhalt der QuelleBullock, Daniel, José L. Contreras-Vidal und 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.
Der volle Inhalt der QuelleZhang, Yicheng, Jiannan Zhao, Mu Hua, Hao Luan, Mei Liu, Fang Lei, Heriberto Cuayahuitl und 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.
Der volle Inhalt der QuelleMudgal, Chhaya, und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "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.
Der volle Inhalt der QuelleHernandez, Daniel, Hendrik Baier und 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.
Der volle Inhalt der QuelleAhumada, 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.
Der volle Inhalt der QuelleGershon, Ron, und 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.
Der volle Inhalt der QuelleZhang, Weinan, Xihuai Wang, Jian Shen und 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.
Der volle Inhalt der QuelleTian, Zheng, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou und 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.
Der volle Inhalt der QuelleHowett, 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.
Der volle Inhalt der QuelleRiley, Patrick, und 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.
Der volle Inhalt der QuelleZafari, Farhad, und 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.
Der volle Inhalt der QuelleMathibela, Bonolo, Ingmar Posner und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "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.
Der volle Inhalt der QuelleBobashev, Georgiy, John Holloway, Eric Solano und Boris Gutkin. A Control Theory Model of Smoking. RTI Press, Juni 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.
Der volle Inhalt der QuelleMillán, Jaime. The Second Generation of Power Exchanges: Lessons for Latin America. Inter-American Development Bank, Dezember 1999. http://dx.doi.org/10.18235/0006812.
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