Dynamic difficulty adjustment (DDA) is a highly significant research direction in game AI. Rubber-banding is one of the DDA techniques extensively used in racing games. This paper improves the poor adaptability of the rubber-banding system combined with the game design and development techniques. An Adaptive Rubber-Banding System (ARBS) based on ARBS relationship curve, modular mechanism, as well as detail processing schemes, is implemented in Unreal Engine 4. The paper builds various test programs and compares each system in terms of the number of players, gameplay mode, and track distance. Experimental results demonstrate that ARBS is more adaptive and provides a better experience for players. Game designers and developers are thus able to employ ARBS to design and implement AI systems suitable for racing games effectively.
Adams, E. (2014). Fundamentals of Game Design. London: Pearson Education.
2.
Astrup, B.K., Danielsen, V., Grønvold, K.L., Hals, A.W.I., Klophmann, A. & Sandnes, F.E. (2019). Car steering in racing games: Similar performance with joysticks and keyboards. In Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (pp. 309–310). doi:10.1145/3316782.3321547.
3.
Baldwin, A., Johnson, D., Wyeth, P. & Sweetser, P. (2013). A framework of dynamic difficulty adjustment in competitive multiplayer video games. In 2013 IEEE International Games Innovation Conference (IGIC) (pp. 16–19). IEEE. doi:10.1109/IGIC.2013.6659150.
4.
Botta, M., Gautieri, V., Loiacono, D. & Lanzi, P.L. (2012). Evolving the optimal racing line in a high-end racing game. In 2012 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 108–115). IEEE.
5.
Cechanowicz, J.E., Gutwin, C., Bateman, S., Mandryk, R. & Stavness, I. (2014). Improving player balancing in racing games. In Proceedings of the First ACM SIGCHI Annual Symposium on Computer–Human Interaction in Play (pp. 47–56). doi:10.1145/2658537.2658701.
6.
Chan, M.T., Chan, C.W. & Gelowitz, C. (2015). Development of a car racing simulator game using artificial intelligence techniques. International Journal of Computer Games Technology, 2015. doi:10.1155/2015/839721.
7.
DeCanio, S.J. (2018). Games between humans and AIs. AI & SOCIETY, 33(4), 557–564. doi:10.1007/s00146-017-0732-5.
8.
Demediuk, S., Tamassia, M., Li, X. & Raffe, W.L. (2019). Challenging ai: Evaluating the effect of mcts-driven dynamic difficulty adjustment on player enjoyment. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1–7).
9.
Fullerton, T. (2019). Game Design Workshop: A Playcentric Approach to Creating Innovative Games. Natick: AK Peters/Boca Raton: CRC Press.
10.
Georgiou, T. & Demiris, Y. (2016). Personalised track design in car racing games. In 2016 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1–8). IEEE.
11.
Georgiou, T. & Demiris, Y. (2017). Adaptive user modelling in car racing games using behavioural and physiological data. User Modeling and User-Adapted Interaction, 27(2), 267–311. doi:10.1007/s11257-017-9192-3.
12.
Granato, M., Gadia, D., Maggiorini, D. & Ripamonti, L.A. (2020). An empirical study of players’ emotions in VR racing games based on a dataset of physiological data. Multimedia Tools and Applications, 79(45), 33657–33686. doi:10.1007/s11042-019-08585-y.
13.
Gregory, J. (2018). Game Engine Architecture. Boca Raton: CRC Press.
14.
Huber, T., Mertes, S., Rangelova, S., Flutura, S. & André, E. (2021). Dynamic difficulty adjustment in virtual reality exergames through experience-driven procedural content generation. arXiv preprint. arXiv:2108.08762.
15.
Ise, A., Umano, M. & Fujimoto, N. (2017). Tuning of fuzzy rules with a real-codec genetic algorithm in car racing game. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) (pp. 1–6). IEEE.
16.
Kristan, D., Bessa, P., Costa, R. & Vaz de Carvalho, C. (2020). Creating competitive opponents for serious games through dynamic difficulty adjustment. Information, 11(3), 156. doi:10.3390/info11030156.
17.
Loiacono, D., Cardamone, L. & Lanzi, P.L. (2011). Automatic track generation for high-end racing games using evolutionary computation. IEEE Transactions on Computational Intelligence and AI in Games, 3(3), 245–259. doi:10.1109/TCIAIG.2011.2163692.
18.
Lopes, R., Eisemann, E. & Bidarra, R. (2017). Authoring adaptive game world generation. IEEE Transactions on Games, 10(1), 42–55. doi:10.1109/TCIAIG.2017.2678759.
19.
MacKinnon, B. (2012). Adaptive AI in a racing simulator: A challenging and fun environment for any user. The Computer Games Journal, 1(1), 71–89. doi:10.1007/BF03392329.
20.
Macklin, C. & Sharp, J. (2016). Games, Design and Play: A Detailed Approach to Iterative Game Design. Boston: Addison-Wesley Professional.
21.
Onieva, E., Pelta, D.A., Milanés, V. & Pérez, J. (2011). A fuzzy-rule-based driving architecture for non-player characters in a car racing game. Soft Computing, 15(8), 1617–1629. doi:10.1007/s00500-011-0691-6.
22.
Or, D.B., Kolomenkin, M. & Shabat, G. (2021). DL-DDA–deep learning based dynamic difficulty adjustment with UX and Gameplay constraints. arXiv preprint. arXiv:2106.03075.
23.
Perez-Liebana, D., Liu, J., Khalifa, A., Gaina, R.D., Togelius, J. & Lucas, S.M. (2019). General video game ai: A multitrack framework for evaluating agents, games, and content generation algorithms. IEEE Transactions on Games, 11(3), 195–214. doi:10.1109/TG.2019.2901021.
24.
Rabin, S. (Ed.) (2013). Game AI Pro: Collected Wisdom of Game AI Professionals. Boca Raton: CRC Press.
25.
Rietveld, A., Bakkes, S. & Roijers, D. (2014). Circuit-adaptive challenge balancing in racing games. In 2014 IEEE Games Media Entertainment (pp. 1–8). IEEE.
26.
Risi, S. & Preuss, M. (2020). Special Issue on AI in Games.
27.
Sazaki, Y., Satria, H. & Syahroyni, M. (2017). Comparison of A* and dynamic pathfinding algorithm with dynamic pathfinding algorithm for NPC on car racing game. In 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA) (pp. 1–6). IEEE.
28.
Tan, C.H., Tan, K.C. & Tay, A. (2011). Dynamic game difficulty scaling using adaptive behavior-based AI. IEEE Transactions on Computational Intelligence and AI in Games, 3(4), 289–301. doi:10.1109/TCIAIG.2011.2158434.
29.
von Willich, J., Schön, D., Günther, S., Müller, F., Mühlhäuser, M. & Funk, M. (2019). VRChairRacer: Using an office chair backrest as a locomotion technique for VR racing games. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–4).
30.
Xue, S., Wu, M., Kolen, J., Aghdaie, N. & Zaman, K.A. (2017). Dynamic difficulty adjustment for maximized engagement in digital games. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 465–471).
31.
Yannakakis, G.N. & Togelius, J. (2018). Artificial Intelligence and Games (Vol. 2). New York: Springer.
32.
Yoon, H., Lee, S., Park, J., Choi, Y. & Cho, S. (2017). Development of racing game using motion seat. In 2017 International Symposium on Ubiquitous Virtual Reality (ISUVR) (pp. 4–7). IEEE.
33.
Zohaib, M. (2018). Dynamic difficulty adjustment (DDA) in computer games: A review. Advances in Human–Computer Interaction, 2018. doi:10.1155/2018/5681652.
