Proposing an Automatic System for Generating Super Mario Bros Datasets
Video games have become a critical part of the entertainment industry. In addition to being designed for fun, games can also be used in education, health, and many other areas. For example, some methods from the Procedural Content Generation (PCG) approach are used to create content for Super Mario Bros (SMB) algorithmically seeking to develop personalized levels based on players’ mechanics. This work aims to provide an automatic system for generating datasets composed of game logs and video frames in the Mario AI Framework, which is expected to speed up the development and validation of PCG, player modeling/profiling, and knowledge tracing methods. This also helps in the reproducibility of research using the SMB game.
Faria, M. P. P., Julia, E. S., Nascimento, M. Z. d., and Julia, R. M. S. (2022). Investigating the performance of various deep neural networks-based approaches designed to identify game events in gameplay footage. Proc. ACM Comput. Graph. Interact. Tech., 5(1).
Green, M. C., Mugrai, L., Khalifa, A., and Togelius, J. (2020). Mario level generation from mechanics using scene stitching. 2020 IEEE Conference on Games, pages 49–56.
Hauck, E. and de Castro Aranha, C. (2020). Automatic generation of super mario levels via graph grammars. 2020 IEEE Conference on Games, pages 297–304.
Karakovskiy, S. and Togelius, J. (2012). The mario ai benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):55–67.
Karpouzis, K., Yannakakis, G. N., Shaker, N., and Asteriadis, S. (2015). The platformer experience dataset. ACII ’15, page 712–718, USA. IEEE Computer Society.
Luo, Z., Guzdial, M., Liao, N., and Riedl, M. (2018). Player experience extraction from gameplay video. CoRR, abs/1809.06201.
Moosa, A. M., Al-Maadeed, N., Saleh, M., Al-Maadeed, S. A., and Aljaam, J. M. (2020). Designing a mobile serious game for raising awareness of diabetic children. IEEE Access, 8:222876–222889.
Sharifzadeh, N., Kharrazi, H., Nazari, E., Tabesh, H., Khodabandeh, M. E., Heidari, S., Tara, M., et al. (2020). Health education serious games targeting health care providers, patients, and public health users: scoping review. JMIR serious games, 8(1):e13459.
Shu, T., Liu, J., and Yannakakis, G. N. (2021). Experience-driven pcg via reinforcement learning: A super mario bros study. 2021 IEEE Conference on Games, pages 1–9.
Summerville, A., Snodgrass, S., Guzdial, M., Holmgard, C., Hoover, A. K., Isaksen, A., Nealen, A., and Togelius, J. (2018). Procedural content generation via machine learning (pcgml). IEEE Transactions on Games, 10(3):257–270.
Svoren, H., Thambawita, V., Halvorsen, P., Jakobsen, P., Garcia-Ceja, E., Noori, F. M., Hammer, H. L., Lux, M., Riegler, M. A., and Hicks, S. A. (2020). Toadstool: A dataset for training emotional intelligent machines playing super mario bros. In Proceedings of the 11th ACM Multimedia Systems Conference, MMSys ’20, page 309–314.
Ullah, M., Amin, S. U., Munsif, M., Safaev, U., Khan, H., Khan, S., and Ullah, H. (2022). Serious games in science education. a systematic literature review. Virtual Reality & Intelligent Hardware, 4(3):189–209.
Wijman, T. (2019). The global games market will generate $152.1 billion in 2019 as the u.s. overtakes china as the biggest market.