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Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 15345-15373. doi: 10.3934/mbe.2023686
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Review

Artificial intelligence for video game visualization, advancements, benefits and challenges

  • 1.
    School of Architecture and Art Design, Hunan University of Science and Technology, Xiangtan 411100, China
  • 2.
    College of Engineering and Design, Hunan Normal University, Changsha 410081, China
  • Received: 17 April 2023 Revised: 28 May 2023 Accepted: 26 June 2023 Published: 21 July 2023

  • In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.

    Citation: Yueliang Wu, Aolong Yi, Chengcheng Ma, Ling Chen. Artificial intelligence for video game visualization, advancements, benefits and challenges[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15345-15373. doi: 10.3934/mbe.2023686

    Related Papers:

  • In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.


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    [1] C. Fairclough, M. Fagan, B. Mac Namee, P. Cunningham, Research Directions for AI in Computer Games, 2001. Available from: http://hdl.handle.net/2262/13098.
    [2] 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, (2016), 1–7. https://doi.org/10.1145/2987491.2987513
    [3] Y. Shi, H. Li, X. Fu, R. Luan, Y. Wang, N. Wang, et al., Self-powered difunctional sensors based on sliding contact-electrification and tribovoltaic effects for pneumatic monitoring and controlling, Nano Energy, 110 (2023), 108339. https://doi.org/10.1016/j.nanoen.2023.108339 doi: 10.1016/j.nanoen.2023.108339
    [4] C. Davis, J. Collins, J. Fraser, H. Zhang, S. Yao, E. Lattanzio, et al., Cave-VR and unity game engine for visualizing city scale 3d meshes, in 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), IEEE, (2022), 733–734. https://doi.org/10.1109/CCNC49033.2022.9700515
    [5] Y. J. Lee, I. Baek, U. Jo, J. Kim, J. Bae, K. Jeong, et al., Self-supervised contrastive learning for predicting game strategies, in IntelliSys 2022: Intelligent Systems and Applications, Springer, (2023), 136–147. https://doi.org/10.1007/978-3-031-16072-1_10
    [6] A. Dachowicz, K. Mall, P. Balasubramani, A. Maheshwari, A. K. Raz, J. H. Panchal, et al., Mission engineering and design using real-time strategy games: An explainable AI approach, J. Mech. Des., 144 (2022), 021710. https://doi.org/10.1115/1.4052841 doi: 10.1115/1.4052841
    [7] S. Sudharsan, M. Educational, Universal real-time strategy game in unreal engine, Int. J. Eng. Appl. Sci. Technol., 6 (2022), 243–248. https://doi.org/10.22214/ijraset.2022.40800 doi: 10.22214/ijraset.2022.40800
    [8] S. Huang, S. Ontañón, C. Bamford, L. Grela, Gym-$\mu$rts: Toward affordable full game real-time strategy games research with deep reinforcement learning, in 2021 IEEE Conference on Games (CoG), IEEE, (2021), 1–8. https://doi.org/10.1109/CoG52621.2021.9619076
    [9] C. Tian, Z. Xu, L. Wang, Y. Liu, Arc fault detection using artificial intelligence: Challenges and benefits, Math. Biosci. Eng., 20 (2023), 12404–12432. https://doi.org/10.3934/mbe.2023552 doi: 10.3934/mbe.2023552
    [10] M. Świechowski, D. Lewiński, R. Tyl, Combining utility ai and mcts towards creating intelligent agents in video games, with the use case of tactical troops: Anthracite shift, in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, (2021), 1–8. https://doi.org/10.1109/SSCI50451.2021.9660170
    [11] R. Graham, H. McCabe, S. Sheridan, Neural networks for real-time pathfinding in computer games, ITB J., 5 (2004), 21. https://doi.org/10.21427/D71Q95 doi: 10.21427/D71Q95
    [12] H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, E. Osawa, H. Matsubara, Robocup: A challenge problem for AI, AI Mag., 18 (1997), 73. https://doi.org/10.1609/aimag.v18i1.1276 doi: 10.1609/aimag.v18i1.1276
    [13] M. O. Riedl, A. Zook, AI for game production, in 2013 IEEE Conference on Computational Inteligence in Games (CIG), IEEE, (2013), 1–8. https://doi.org/10.1109/CIG.2013.6633663
    [14] C. H. Tan, K. C. Tan, A. Tay, Dynamic game difficulty scaling using adaptive behavior-based AI, IEEE Trans. Comput. Intell. AI Games, 3 (2011), 289–301. https://doi.org/10.1109/TCIAIG.2011.2158434 doi: 10.1109/TCIAIG.2011.2158434
    [15] R. Rahim, N. Kurniasih, A. Hasibuan, L. Andriany, A. Najmurrokhman, S. Supriyanto, et al., Congklak, a traditional game solution approach with breadth first search, in MATEC Web of Conferences, EDP Sciences, 197 (2018), 03007. https://doi.org/10.1051/matecconf/201819703007
    [16] Y. Shi, L. Li, J. Yang, Y. Wang, S. Hao, Center-based transfer feature learning with classifier adaptation for surface defect recognition, Mech. Syst. Signal Process., 188 (2023), 110001. https://doi.org/10.1016/j.ymssp.2022.110001 doi: 10.1016/j.ymssp.2022.110001
    [17] R. M. Sindhu, L. S. P. Annabel, G. Monisha, Development of a 2D game using artificial intelligence in unity, in 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, (2022), 1031–1037. https://doi.org/10.1109/ICOEI53556.2022.9776750
    [18] Z. Liu, D. Yang, Y. Wang, M. Lu, R. Li, Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks, Appl. Soft Comput., 135 (2023), 110040. https://doi.org/10.1016/j.asoc.2023.110040 doi: 10.1016/j.asoc.2023.110040
    [19] M. Treanor, A. Zook, M. P. Eladhari, J. Togelius, G. Smith, M. Cook, et al., AI-based game design patterns, in Proceedings of the 10th International Conference on the Foundations of Digital Games 2015 (FDG 2015), 2015.
    [20] S. Agarwal, G. Wallner, F. Beck, Bombalytics: Visualization of competition and collaboration strategies of players in a bomb laying game, Comput. Graphics Forum, 39 (2020), 89–100. https://doi.org/10.1111/cgf.13965 doi: 10.1111/cgf.13965
    [21] Y. Wang, Z. Liu, J. Xu, W. Yan, Heterogeneous network representation learning approach for ethereum identity identification, IEEE Trans. Comput. Social Syst., 10 (2023), 890–899. https://doi.org/10.1109/TCSS.2022.3164719 doi: 10.1109/TCSS.2022.3164719
    [22] P. J. Kumar, W. Hu, M. Pan, X. Li, Detonation of artificial intelligence for virtual reality based 3D game design, Proc. IEEE, 11 (2016), 242–253.
    [23] H. Yu, Y. Ge, Design and analysis of clothing catwalks taking into account unity's immersive virtual reality in an artificial intelligence environment, Comput. Intell. Neurosci., 2022 (2022). https://doi.org/10.1155/2022/2861767
    [24] D. Aversa, Unity Artificial Intelligence Programming: Add powerful, Believable, and Fun AI Entities in Your Game with the Power of Unity, Packt Publishing Ltd, 2022.
    [25] M. P. Schijven, T. Kikkawa, Is there any (artificial) intelligence in gaming, 53 (2022). https://doi.org/10.1177/10468781221101685
    [26] E. J. Pretty, H. M. Fayek, F. Zambetta, A case for personalized non-player character companion design, Int. J. Hum. Comput. Interact., 2023 (2023), 1–20. https://doi.org/10.1080/10447318.2023.2181125 doi: 10.1080/10447318.2023.2181125
    [27] E. Plastino, M. Purdy, Game changing value from artificial intelligence: eight strategies, Strat. Leadersh., 46 (2018), 16–22. https://doi.org/10.1108/SL-11-2017-0106 doi: 10.1108/SL-11-2017-0106
    [28] I. Millington, J. Funge, Artificial Intelligence for Games, CRC Press, 2018.
    [29] M. Bowling, J. Fürnkranz, T. Graepel, R. Musick, Machine learning and games, Mach. Learn., 63 (2006), 211–215. https://doi.org/10.1007/s10994-006-8919-x
    [30] Z. Zhou, Automatic machine learning-based data analysis for video game industry, in 2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE, (2022), 732–737. https://doi.org/10.1109/CEI57409.2022.9950194
    [31] W. Qi, H. Fan, H. R. Karimi, H. Su, An adaptive reinforcement learning-based multimodal data fusion framework for human-robot confrontation gaming, Neural Networks, 164 (2023), 489–496. https://doi.org/10.1016/j.neunet.2023.04.043 doi: 10.1016/j.neunet.2023.04.043
    [32] P. I. Cowling, C. D. Ward, E. J. Powley, Ensemble determinization in monte carlo tree search for the imperfect information card game magic: The gathering, IEEE Trans. Comput. Intell. AI Games, 4 (2012), 241–257. https://doi.org/10.1109/TCIAIG.2012.2204883 doi: 10.1109/TCIAIG.2012.2204883
    [33] P. Sweetser, J. Wiles, Current AI in games: A review, Aust. J. Intell. Inf. Process. Syst., 8 (2002), 24–42.
    [34] F. R. Miranda, J. E. Kögler Jr, E. D. M. Hernandez, M. L. Netto, An artificial life approach for the animation of cognitive characters, Comput. Graph., 25 (2001), 955–964. https://doi.org/10.1016/S0097-8493(01)00151-0 doi: 10.1016/S0097-8493(01)00151-0
    [35] A. M. Smith, M. Mateas, Answer set programming for procedural content generation: A design space approach, IEEE Trans. Comput. Intell. AI Games, 3 (2011), 187–200. https://doi.org/10.1109/TCIAIG.2011.2158545 doi: 10.1109/TCIAIG.2011.2158545
    [36] F. Mourato, M. P. dos Santos, F. Birra, Automatic level generation for platform videogames using genetic algorithms, in Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, (2011), 1–8. https://doi.org/10.1145/2071423.2071433
    [37] M. Frade, F. F. de Vega, C. Cotta, Automatic evolution of programs for procedural generation of terrains for video games: Accessibility and edge length constraints, Soft Comput., 16 (2012), 1893–1914. https://doi.org/10.1007/s00500-012-0863-z doi: 10.1007/s00500-012-0863-z
    [38] W. Qi, H. Su, A cybertwin based multimodal network for ecg patterns monitoring using deep learning, IEEE Trans. Ind. Inf., 18 (2022), 6663–6670. https://doi.org/10.1109/TII.2022.3159583 doi: 10.1109/TII.2022.3159583
    [39] H. Su, W. Qi, J. Chen, D. Zhang, Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint, IEEE Trans. Fuzzy Syst., 30 (2022), 1564–1573. https://doi.org/10.1109/TFUZZ.2022.3157075 doi: 10.1109/TFUZZ.2022.3157075
    [40] M. El Beheiry, S. Doutreligne, C. Caporal, C. Ostertag, M. Dahan, J. B. Masson, Virtual reality: beyond visualization, J. Mol. Biol., 431 (2019), 1315–1321. https://doi.org/10.1016/j.jmb.2019.01.033 doi: 10.1016/j.jmb.2019.01.033
    [41] K. Shao, Z. Tang, Y. Zhu, N. Li, D. Zhao, A survey of deep reinforcement learning in video games, 2019, preprint, arXiv: 1912.10944. https://doi.org/10.48550/arXiv.1912.10944
    [42] H. Kolivand, M. S. Sunar, A. Altameem, A. Rehman, M. Uddin, Shadow mapping algorithms: applications and limitations, Appl. Math. Inf. Sci., 9 (2015), 1307–1315.
    [43] D. Minh, H. X. Wang, Y. F. Li, T. N. Nguyen, Explainable artificial intelligence: a comprehensive review, Artif. Intell. Rev., 55 (2022), 3503–3568. https://doi.org/10.1007/s10462-021-10088-y doi: 10.1007/s10462-021-10088-y
    [44] S. A. Seshia, D. Sadigh, S. S. Sastry, Toward verified artificial intelligence, Commun. ACM, 65 (2022), 46–55. https://doi.org/10.1145/3503914 doi: 10.1145/3503914
    [45] C. Meske, E. Bunde, J. Schneider, M. Gersch, Explainable artificial intelligence: objectives, stakeholders, and future research opportunities, Inf. Syst. Manage., 39 (2022), 53–63. https://doi.org/10.1080/10580530.2020.1849465 doi: 10.1080/10580530.2020.1849465
    [46] L. Chan, L. Hogaboam, R. Cao, Artificial intelligence in video games and esports, in Applied Artificial Intelligence in Business, Springer, (2022), 335–352. https://doi.org/10.1007/978-3-031-05740-3_22
    [47] H. K. Raharjo, N. F. Rosyidin, T. W. Purboyo, R. A. Nugrahaeni, Application of artificial intelligence in indonesian debate education game, CEPAT J. Comput. Eng. Prog. Appl. Technol., 1 (2022), 37–46. https://doi.org/10.25124/cepat.v1i03.5393 doi: 10.25124/cepat.v1i03.5393
    [48] J. Hartmann, S. DiVerdi, C. Nguyen, D. Vogel, View-dependent effects for 360 virtual reality video, in Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, (2020), 354–364. https://doi.org/10.1145/3379337.3415846
    [49] C. Plut, P. Pasquier, J. Ens, R. Tchemeube, PreGLAM-MMM: Application and evaluation of affective adaptive generative music in video games, in Proceedings of the 17th International Conference on the Foundations of Digital Games, (2022), 1–11. https://doi.org/10.1145/3555858.3555947
    [50] S. Seidel, N. Berente, A. Lindberg, K. Lyytinen, B. Martinez, J. V. Nickerson, Artificial intelligence and video game creation: A framework for the new logic of autonomous design, J. Digit. Social Res., 2 (2020), 126–157. https://doi.org/10.33621/jdsr.v2i3.46 doi: 10.33621/jdsr.v2i3.46
    [51] J. E. Cruz-Tapia, H. H. Ganoza-Fuentes, G. M. Garcia-Jimenez, S. E. Cieza-Mostacero, Ukucha: a video game for teaching experimental psychology, in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), IEEE, (2022), 1–6. https://doi.org/10.23919/CISTI54924.2022.9820359
    [52] A. Smerdov, A. Somov, E. Burnaev, B. Zhou, P. Lukowicz, Detecting video game player burnout with the use of sensor data and machine learning, IEEE Internet Things J., 8 (2021), 16680–16691. https://doi.org/10.1109/JIOT.2021.3074740 doi: 10.1109/JIOT.2021.3074740
    [53] J. Willard, X. Jia, S. Xu, M. Steinbach, V. Kumar, Integrating physics-based modeling with machine learning: A survey, preprint, arXiv: 2003.04919, 2020.
    [54] H. Su, W. Qi, Y. Hu, H. R. Karimi, G. Ferrigno, E. De Momi, An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators, IEEE Trans. Ind. Inf., 18 (2020), 1864–1872. https://doi.org/10.1109/TII.2020.3036693 doi: 10.1109/TII.2020.3036693
    [55] $\tilde{\mathrm{L}}$. Nowak, A. B$\tilde{\mathrm{k}}$ak, T. Czajkowski, K. Wojciechowski, Modeling and rendering of volumetric clouds in real-time with unreal engine 4, in ICCVG 2018: Computer Vision and Graphics, Springer, 11114 (2018), 68–78. https://doi.org/10.1007/978-3-030-00692-1_7
    [56] C. Koniaris, M. Kosek, D. Sinclair, K. Mitchell, Compressed animated light fields with real-time view-dependent reconstruction, IEEE Trans. Visual Comput. Graphics, 25 (2018), 1666–1680. https://doi.org/10.1109/TVCG.2018.2818156 doi: 10.1109/TVCG.2018.2818156
    [57] F. Abbas, M. Malah, M. C. Babahenini, Approximating global illumination with ambient occlusion and environment light via generative adversarial networks, Pattern Recognit. Lett., 166 (2023), 209–217. https://doi.org/10.1016/j.patrec.2022.12.007 doi: 10.1016/j.patrec.2022.12.007
    [58] R. R. Torrado, A. Khalifa, M. C. Green, N. Justesen, S. Risi, J. Togelius, Bootstrapping conditional gans for video game level generation, in 2020 IEEE Conference on Games (CoG), IEEE, (2020), 41–48. https://doi.org/10.1109/CoG47356.2020.9231576
    [59] M. Pichlmair, M. Johansen, Designing game feel: A survey, IEEE Trans. Games, 14 (2021), 138–152. https://doi.org/10.1109/TG.2021.3072241 doi: 10.1109/TG.2021.3072241
    [60] I. Evin, P. Hämäläinen, C. Guckelsberger, Cine-AI: Generating video game cutscenes in the style of human directors, preprint, arXiv: 2208.05701. https://doi.org/10.48550/arXiv.2208.05701
    [61] E. Micheloni, M. Tramarin, A. Rodà, F. Chiaravalli, Playing to play: a piano-based user interface for music education video-games, Multimedia Tools Appl., 78 (2019), 13713–13730. https://doi.org/10.1007/s11042-018-6917-1 doi: 10.1007/s11042-018-6917-1
    [62] V. Nair, J. L. Karp, S. Silverman, M. Kalra, H. Lehv, F. Jamil, et al., Navstick: Making video games blind-accessible via the ability to look around, in The 34th Annual ACM Symposium on User Interface Software and Technology, (2021), 538–551. https://doi.org/10.1145/3472749.3474768
    [63] Y. A. Shim, K. Park, S. Lee, J. Son, T. Woo, G. Lee, Fs-pad: Video game interactions using force feedback gamepad, in Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, (2020), 938–950. https://doi.org/10.1145/3379337.3415850
    [64] A. Petrovas, R. Bausys, Procedural video game scene generation by genetic and neutrosophic waspas algorithms, Appl. Sci., 12 (2022), 772. https://doi.org/10.3390/app12020772 doi: 10.3390/app12020772
    [65] J. M. L. Asensio, J. Peralta, R. Arrabales, M. G. Bedia, P. Cortez, A. L. Peña, Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters, Expert Syst. Appl., 41 (2014), 7281–7290. https://doi.org/10.1016/j.eswa.2014.05.004 doi: 10.1016/j.eswa.2014.05.004
    [66] X. Fan, J. Wu, L. Tian, A review of artificial intelligence for games, in Artificial Intelligence in China, 572 (2020), 298–303. https://doi.org/10.1007/978-981-15-0187-6_34
    [67] G. N. Yannakakis, J. Togelius, Game AI panorama, in Artificial Intelligence and Games, Springer, (2018), 259–277. https://doi.org/10.1007/978-3-319-63519-4_6
    [68] H. Su, A. Mariani, S. E. Ovur, A. Menciassi, G. Ferrigno, E. De Momi, Toward teaching by demonstration for robot-assisted minimally invasive surgery, IEEE Trans. Autom. Sci. Eng., 18 (2021), 484–494. https://doi.org/10.1109/TASE.2020.3045655 doi: 10.1109/TASE.2020.3045655
    [69] W. Qi, A. Aliverti, A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity, IEEE J. Biomed. Health Inf., 24 (2019), 2199–2207. https://doi.org/10.1109/JBHI.2019.2963048 doi: 10.1109/JBHI.2019.2963048
    [70] W. C. Stirling, Satisficing Games and Decision Making: with Applications to Engineering and Computer Science, Cambridge University Press, 2003.
    [71] H. Kaindl, Tree searching algorithms, in Computers, Chess, and Cognition, (1990), 133–158. https://doi.org/10.1007/978-1-4613-9080-0_8
    [72] J. C. Lian, Y. Si, T. Huang, W. Q. Huang, W. Hu, G. F. Huang, Highly efficient tree search algorithm for irreducible site-occupancy configurations, Phys. Rev. B, 105 (2022), 014201. https://doi.org/10.1103/PhysRevB.105.014201 doi: 10.1103/PhysRevB.105.014201
    [73] P. Borovska, M. Lazarova, Efficiency of parallel minimax algorithm for game tree search, in Proceedings of the 2007 International Conference on Computer Systems and Technologies, (2007), 1–6. https://doi.org/10.1145/1330598.1330615
    [74] A. Saffidine, H. Finnsson, M. Buro, Alpha-beta pruning for games with simultaneous moves, in 26th AAAI Conference on Artificial Intelligence, 26 (2012). https://doi.org/10.1609/aaai.v26i1.8148
    [75] C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, et al., A survey of monte carlo tree search methods, IEEE Trans. Comput. Intell. AI Games, 4 (2012), 1–43. https://doi.org/10.1109/TCIAIG.2012.2186810 doi: 10.1109/TCIAIG.2012.2186810
    [76] V. N. Rao, V. Kumar, Parallel depth first search. part i. implementation, Int. J. Parallel Program., 16 (1987), 479–499. https://doi.org/10.1007/BF01389000 doi: 10.1007/BF01389000
    [77] V. Kumar, V. N. Rao, Parallel depth first search. part ii. analysis, Int. J. Parallel Program., 16 (1987), 501–519. https://doi.org/10.1007/BF01389001 doi: 10.1007/BF01389001
    [78] A. Bundy, L. Wallen, Breadth-first search, in Catalogue of Artificial Intelligence Tools, Springer, (1986), 13. https://doi.org/10.1007/978-3-642-96964-5_1
    [79] W. M. Spears, K. A. De Jong, T. Bäck, D. B. Fogel, H. De Garis, An overview of evolutionary computation, in Machine Learning: ECML-93, Springer, (1993), 442–459. https://doi.org/10.1007/3-540-56602-3_163
    [80] H. B. Amor, A. Rettinger, Intelligent exploration for genetic algorithms: Using self-organizing maps in evolutionary computation, in Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, (2005), 1531–1538. https://doi.org/10.1145/1068009.1068250
    [81] S. Mirjalili, Evolutionary Algorithms and Neural Networks, Springer, 2019. https://doi.org/10.1007/978-3-319-93025-1
    [82] M. Buro, Improving heuristic mini-max search by supervised learning, Artif. Intell., 134 (2002), 85–99. https://doi.org/10.1016/S0004-3702(01)00093-5 doi: 10.1016/S0004-3702(01)00093-5
    [83] M. E. Celebi, K. Aydin, Unsupervised Learning Algorithms, Springer, 2016. https://doi.org/10.1007/978-3-319-24211-8
    [84] R. R. Torrado, P. Bontrager, J. Togelius, J. Liu, D. Perez-Liebana, Deep reinforcement learning for general video game AI, in 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, (2018), 1–8. https://doi.org/10.1109/CIG.2018.8490422
    [85] G. Lample, D. S. Chaplot, Playing FPS games with deep reinforcement learning, in 31st AAAI Conference on Artificial Intelligence, 31 (2017). https://doi.org/10.1609/aaai.v31i1.10827
    [86] M. Kempka, M. Wydmuch, G. Runc, J. Toczek, W. Jaśkowski, Vizdoom: A doom-based AI research platform for visual reinforcement learning, in 2016 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, (2016), 1–8. https://doi.org/10.1109/CIG.2016.7860433
    [87] N. Justesen, P. Bontrager, J. Togelius, S. Risi, Deep learning for video game playing, IEEE Trans. Games, 12 (2019), 1–20. https://doi.org/10.1109/TG.2019.2896986 doi: 10.1109/TG.2019.2896986
    [88] W. Qi, S. E. Ovur, Z. Li, A. Marzullo, R. Song, Multi-sensor guided hand gesture recognition for a teleoperated robot using a recurrent neural network, IEEE Rob. Autom. Lett., 6 (2021), 6039–6045. https://doi.org/10.1109/LRA.2021.3089999 doi: 10.1109/LRA.2021.3089999
    [89] R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, J. P. Campbell, Introduction to machine learning, neural networks, and deep learning, Transl. Vision Sci. Technol., 9 (2020), 14.
    [90] K. Kumar, G. S. M. Thakur, Advanced applications of neural networks and artificial intelligence: A review, Int. J. Inf. Technol. Comput. Sci., 4 (2012), 57. https://doi.org/10.5815/ijitcs.2012.06.08 doi: 10.5815/ijitcs.2012.06.08
    [91] H. Su, W. Qi, Y. Schmirander, S. E. Ovur, S. Cai, X. Xiong, A human activity-aware shared control solution for medical human-robot interaction, Assem. Autom., 42 (2022), 388–394. https://doi.org/10.1108/AA-12-2021-0174 doi: 10.1108/AA-12-2021-0174
    [92] T. A. Jarrell, Y. Wang, A. E. Bloniarz, C. A. Brittin, M. Xu, J. N. Thomson, et al., The connectome of a decision-making neural network, Science, 337 (2012), 437–444. https://doi.org/10.1126/science.1221762 doi: 10.1126/science.1221762
    [93] Y. Y. Lee, J. T. Kim, G. Y. Yun, The neural network predictive model for heat island intensity in seoul, Energy Build., 110 (2016), 353–361. https://doi.org/10.1016/j.enbuild.2015.11.013 doi: 10.1016/j.enbuild.2015.11.013
    [94] J. C. Bezdek, On the relationship between neural networks, pattern recognition and intelligence, Int. J. Approximate Reasoning, 6 (1992), 85–107. https://doi.org/10.1016/0888-613X(92)90013-P doi: 10.1016/0888-613X(92)90013-P
    [95] Y. Goldberg, Neural Network Methods for Natural Language Processing, 2017. https://doi.org/10.2200/S00762ED1V01Y201703HLT037
    [96] K. Khantanapoka, K. Chinnasarn, Pathfinding of 2D & 3D game real-time strategy with depth direction A* algorithm for multi-layer, in 2009 Eighth International Symposium on Natural Language Processing, IEEE, (2009), 184–188. https://doi.org/10.1109/SNLP.2009.5340922
    [97] L. Bertolini, Hands-On Game Development without Coding: Create 2D and 3D games with Visual Scripting in Unity, Packt Publishing Ltd, 2018.
    [98] X. Zhu, Behavior tree design of intelligent behavior of non-player character (NPC) based on unity3D, J. Intell. Fuzzy Syst., 37 (2019), 6071–6079. https://doi.org/10.3233/JIFS-179190 doi: 10.3233/JIFS-179190
    [99] M. Chover, C. Marín, C. Rebollo, I. Remolar, A game engine designed to simplify 2D video game development, Multimedia Tools Appl., 79 (2020), 12307–12328. https://doi.org/10.1007/s11042-019-08433-z doi: 10.1007/s11042-019-08433-z
    [100] X. Xu, M. Huang, K. Zou, Automatic generated navigation mesh algorithm on 3D game scene, Procedia Eng., 15 (2011), 3215–3219. https://doi.org/10.1016/j.proeng.2011.08.604 doi: 10.1016/j.proeng.2011.08.604
    [101] J. Y. Wang, Classification of humans and bots in two typical two-player computer games, in 2018 3rd International Conference on Computer and Communication Systems (ICCCS), IEEE, (2018), 502–505. https://doi.org/10.1109/CCOMS.2018.8463277
    [102] M. Cavazza, S. Bandi, I. Palmer, Situated ai in video games: Integrating NLP, path planning, and 3D animation, in AAAI 1999 Spring Symposium on Artificial Intelligence and Computer Games, (1999), 6–12.
    [103] C. Marín-Lora, M. Chover, J. M. Sotoca, A multi-agent specification for the tetris game, in Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021, Springer, (2022), 169–178. https://doi.org/10.1007/978-3-030-86261-9_17
    [104] M. Miranda, A. A. Sánchez-Ruiz, F. Peinado, A CBR approach for imitating human playing style in Ms. Pac-Man video game, in ICCBR 2018: Case-Based Reasoning Research and Development, Springer, (2018), 292–308. https://doi.org/10.1007/978-3-030-01081-2_20
    [105] O. Drageset, M. H. Winands, R. D. Gaina, D. Perez-Liebana, Optimising level generators for general video game AI, in 2019 IEEE Conference on Games (CoG), IEEE, (2019), 1–8. https://doi.org/10.1109/CIG.2019.8847961
    [106] A. Fadaeddini, B. Majidi, M. Eshghi, A case study of generative adversarial networks for procedural synthesis of original textures in video games, in 2018 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC), IEEE, (2018), 118–122. https://doi.org/10.1109/DGRC.2018.8712070
    [107] C. Murphy, S. Mudur, D. Holden, M. A. Carbonneau, D. Ghafourzadeh, A. Beauchamp, Artist guided generation of video game production quality face textures, Comput. Graphics, 98 (2021), 268–279. https://doi.org/10.1016/j.cag.2021.06.004 doi: 10.1016/j.cag.2021.06.004
    [108] Y. Zhong, Analysis of convolution neural network technology on the visual effect of the game, in 2021 International Conference on Information Science, Parallel and Distributed Systems (ISPDS), IEEE, (2021), 75–78. https://doi.org/10.1109/ISPDS54097.2021.00021
    [109] L. Xiao, S. Nouri, M. Chapman, A. Fix, D. Lanman, A. Kaplanyan, Neural supersampling for real-time rendering, ACM Trans. Graphics, 39 (2020), 142:1–142:12. https://doi.org/10.1145/3386569.3392376 doi: 10.1145/3386569.3392376
    [110] E. S. de Lima, B. Feijó, A. L. Furtado, Video-based interactive storytelling using real-time video compositing techniques, Multimedia Tools Appl., 77 (2018), 2333–2357. https://doi.org/10.1007/s11042-017-4423-5 doi: 10.1007/s11042-017-4423-5
    [111] A. Nawalagatti, R. K. Prakash, A comprehensive review on artificial intelligence based machine learning techniques for designing interactive characters, Int. J. Math. Comput. Appl. Res. (IJMCAR), 8 (2018), 1–10.
    [112] S. Gujrania, D. Long, B. Magerko, Moving in virtual space: A laban-inspired framework for procedural animation, in Proceedings of the Experimental AI in Games Workshop (EXAG) at the 15th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'19), 2019.
    [113] M. Li, Z. Sun, Z. Jiang, Z. Tan, J. Chen, A virtual reality platform for safety training in coal mines with ai and cloud computing, Discrete Dyn. Nat. Soc., 2020 (2020), 1–7. https://doi.org/10.1155/2020/6243085 doi: 10.1155/2020/6243085
    [114] N. Anantrasirichai, D. Bull, Artificial intelligence in the creative industries: a review, Artif. Intell. Rev., 55 (2022), 589–656. https://doi.org/10.1007/s10462-021-10039-7 doi: 10.1007/s10462-021-10039-7
    [115] M. Khanian, 3D Reconstruction Using Generalized Perspective Photometric Stereo, PhD thesis, BTU Cottbus-Senftenberg, 2018.
    [116] J. Li, J. Xu, F. Zhong, X. Kong, Y. Qiao, Y. Wang, Pose-assisted multi-camera collaboration for active object tracking, in Proceedings of the AAAI Conference on Artificial Intelligence, 34 (2020), 759–766. https://doi.org/10.1609/aaai.v34i01.5419
    [117] J. Roettl, R. Terlutter, The same video game in 2D, 3D or virtual reality–how does technology impact game evaluation and brand placements, PLoS ONE, 13 (2018), e0200724. https://doi.org/10.1371/journal.pone.0200724 doi: 10.1371/journal.pone.0200724
    [118] F. Siam, Z. I. Prince, A. N. Bari, Anti-Aliasing for Real-Time Applications in 3D Using Deep Convolutional Neural Network, PhD thesis, Brac University, 2020.
    [119] W. Qiu, A. Yuille, Unrealcv: Connecting computer vision to unreal engine, in Computer Vision-ECCV 2016 Workshops, Springer, (2016), 909–916. https://doi.org/10.1007/978-3-319-49409-8_75
    [120] A. Fink, J. Denzinger, J. Aycock, Extracting npc behavior from computer games using computer vision and machine learning techniques, in 2007 IEEE Symposium on Computational Intelligence and Games, IEEE, (2007), 24–31. https://doi.org/10.1109/CIG.2007.368075
    [121] H. T. Joo, K. J. Kim, Visualization of deep reinforcement learning using grad-cam: how AI plays atari games, in 2019 IEEE Conference on Games (CoG), IEEE, (2019), 1–2. https://doi.org/10.1109/CIG.2019.8847950
    [122] A. Aamir, M. Tamosiunaite, F. Wörgötter, Caffe2unity: Immersive visualization and interpretation of deep neural networks, Electronics, 11 (2021), 83. https://doi.org/10.3390/electronics11010083 doi: 10.3390/electronics11010083
    [123] K. Sorochan, M. Guzdial, Generating real-time strategy game units using search-based procedural content generation and monte carlo tree search, preprint, arXiv: 2212.03387. https://doi.org/10.48550/arXiv.2212.03387
    [124] L. Xu, J. Hurtado-Grueso, D. Jeurissen, D. P. Liebana, A. Dockhorn, Elastic monte carlo tree search with state abstraction for strategy game playing, preprint, arXiv: 2205.15126. https://doi.org/10.1109/CoG51982.2022.9893587
    [125] M. Crippa, P. L. Lanzi, F. Marocchi, An analysis of single-player Monte Carlo Tree Search performance in Sokoban, Expert Syst. Appl., 192 (2022), 116224. https://doi.org/10.1016/j.eswa.2021.116224 doi: 10.1016/j.eswa.2021.116224
    [126] S. Yoshida, M. Ishihara, T. Miyazaki, Y. Nakagawa, T. Harada, R. Thawonmas, Application of monte-carlo tree search in a fighting game AI, in 2016 IEEE 5th Global Conference on Consumer Electronics, IEEE, (2016), 1–2. https://doi.org/10.1109/GCCE.2016.7800536
    [127] S. Hyun, Y. Kim, C. J. Park, Development of a game content based on metaverse providing decision tree algorithm education for middle school students, J. Korea Contents Assoc., 22 (2022), 106–117.
    [128] Y. Ma, S. Liu, Performance comparison of algorithms in cake cutting game, in Proceedings of the 2021 International Conference on Social Development and Media Communication (SDMC 2021), Atlantis Press, (2022), 17–22. https://doi.org/10.2991/assehr.k.220105.005
    [129] N. Nasri, S. Orts-Escolano, M. Cazorla, An semg-controlled 3D game for rehabilitation therapies: Real-time time hand gesture recognition using deep learning techniques, Sensors, 20 (2020), 6451. https://doi.org/10.3390/s20226451 doi: 10.3390/s20226451
    [130] J. Zhu, J. Villareale, N. Javvaji, S. Risi, M. Löwe, R. Weigelt, et al., Player-AI interaction: What neural network games reveal about AI as play, in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, (2021), 1–17. https://doi.org/10.1145/3411764.3445307
    [131] G. N. Yannakakis, Game AI revisited, in Proceedings of the 9th conference on Computing Frontiers, (2012), 285–292. https://doi.org/10.1145/2212908.2212954
    [132] B. Kim, J. Park, J. Suh, Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information, Decis. Support Syst., 134 (2020), 113302. https://doi.org/10.1016/j.dss.2020.113302 doi: 10.1016/j.dss.2020.113302
    [133] H. T. Kim, K. J. Kim, Hybrid of rule-based systems using genetic algorithm to improve platform game performance, Procedia Comput. Sci., 24 (2013), 114–120. https://doi.org/10.1016/j.procs.2013.10.033 doi: 10.1016/j.procs.2013.10.033
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