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Method for the Player Profiling in the Turn-based Computer Games

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels.
Rocznik
Strony
461--468
Opis fizyczny
Bibliogr. 22 poz., fot., rys., tab., wykr.
Twórcy
autor
  • Warsaw University of Technology, Poland
  • Warsaw University of Life Sciences, Poland
  • SGH Warsaw School of Economics, Poland
Bibliografia
  • [1] Guinness World Records. 2015. Most Participants on a Single-Player Online Videogame. Internet. (2015), http://www.guinnessworldrecords.com/world-records/most-participantson-a-single-player-online-videogame, retr.17/04/2017.
  • [2] G. N. Yannakakis & J. Hallam, “Real-time game adaptation for optimizing player satisfaction.”, in IEEE Transactions on Computational Intelligence and AI in Games, 1(2), pp 121-133, 2009. https://doi.org/10.1109/TCIAIG.2009.2024533
  • [3] C. Pedersen, J. Togelius & G N. Yannakakis, “Modeling player experience for content creation.”, IEEE Transactions on Computational Intelligence and AI in Games, 2(1), pp 54-67, 2010. https://doi.org/10.1109/TCIAIG.2010.2043950
  • [4] N. Shaker, G. N. Yannakakis & J. Togelius, “Towards Automatic Personalized Content Generation for Platform Games.”, in AIIDE, 2010.
  • [5] N. Shaker, G. N. Yannakakis, J. Togelius, M. Nicolau & M. O'neill, “Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution,” in AIIDE, 2012. https://doi.org/10.1609/aiide.v8i1.12501
  • [6] J. Pita, B. Magerko & S. Brodie, “True story: dynamically generated, contextually linked quests in persistent systems.”, in Proceedings of the 2007 conference on Future Play, pp. 145-151, ACM, 2007. https://doi.org/10.1145/1328202.1328228
  • [7] G. A. Martin, C. E. Hughes, S. Schatz & D. Nicholson, “The use of functional L-systems for scenario generation in serious games.”, in Proceedings of the 2010 Workshop on Procedural Content Generation in Games, p. 6, ACM, 2010. https://doi.org/10.1145/1814256.1814262
  • [8] K. Conley & D. Perry, “How does he saw me? A recommendation engine for picking heroes in Dota 2.”, Np, nd Web, 7, 2013.
  • [9] W. Looi, M. Dhaliwal, R. Alhajj & J. Rokne, “Recommender System for Items in Dota 2.”, IEEE Transactions on Computational Intelligence and AI in Games, 2018. https://doi.org/10.1109/TG.2018.2844121
  • [10] C. Eggert, M. Herrlich, J. Smeddinck & R. Malaka, “Classification of player roles in the team-based multi-player game dota 2.”, in International Conference on Entertainment Computing, pp. 112125, Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-24589-8_9
  • [11] N. Kinkade, L. Jolla & K. Lim, “Dota 2 win prediction.”, Technical Report, tech. rep., University of California San Diego, 2015.
  • [12] M. Waltham & D. Moodley, “An analysis of artificial intelligence techniques in multiplayer online battle arena game environments.”, in Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, p. 45, ACM, 2016. https://doi.org/10.1145/2987491.2987513
  • [13] A. Semenov, P. Romov, K. Neklyudov, D. Yashkov & D. Kireev, “Applications of Machine Learning in Dota 2: Literature Review and Practical Knowledge Sharing.”, 2016.
  • [14] R. Hunicke, “The case for dynamic difficulty adjustment in games.”, in Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, pp. 429-433, ACM, 2005. https://doi.org/10.1145/1178477.1178573
  • [15] R. Lopes & R. Bidarra, “Adaptivity challenges in games and simulations: a survey.”, IEEE Transactions on Computational Intelligence and AI in Games, 3(2), pp 85-99, 2011. https://doi.org/10.1109/TCIAIG.2011.2152841
  • [16] D. Maynes-Aminzade, R. Pausch & S. Seitz, “Techniques for interactive audience participation.”, in Proceedings of the 4th IEEE International Conference on Multimodal Interfaces, p. 15, IEEE Computer Society, 2002. https://doi.org/10.1109/ICMI.2002.1166962
  • [17] P. Lessel, A. Vielhauer & A. Krüger, “CrowdChess: A System to Investigate Shared Game Control in Live-Streams.” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play, pp. 389-400, ACM, 2017. https://doi.org/10.1145/3116595.3116597
  • [18] P. Lessel, M. Mauderer, C. Wolff & A. Krüger, ”Let's Play My Way: Investigating Audience Influence in User-Generated Gaming Live-Streams.”, in Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video, pp. 5163, ACM, 2017. https://doi.org/10.1145/3077548.3077556
  • [19] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi,... & D. Sampath, “The YouTube video recommendation system.”, in Proceedings of the fourth ACM conference on Recommender systems, pp. 293-296, ACM, 2010. https://doi.org/10.1145/1864708.1864770
  • [20] Cherrypick Games web page: http://cherrypickgames.com/, (accessed 20.08.2018).
  • [21] App Store webpage of MatchUp Friends game: https://itunes.apple.com/pl/app/matchup-friends-find-pairs/id1133956564?l=pl&mt=8 (Accesed: 28.08.2018).
  • [22] A. Drachen, R. Sifa, "Clustering Game Behavior Data," IEEE Transactions on Computational Intelligence and AI in Games, Vol. 7, No. 3, 2015, pp. 266-278, https://doi.org/10.1109/TCIAIG.2014.2376982
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-79be100e-909d-4232-ab4f-e077e3ac4bfe
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