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Threat Construction for Dynamic Enemy Status in a Platformer Game using Classical Genetic Algorithm
Corresponding Author(s) : Ardiawan Bagus Harisa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 3, August 2023
Abstract
Digital game genre such as Action-Platformer is widely popular among buyers on a platform like Steam. The non-playable character enemies in the game are important in action games. Unfortunately, they usually have static attributes like health points, damage, and enemy movement. Using the combination of procedural content generation and dynamic difficulty adjustment with a classical genetic algorithm, we drive the threat value of a platform to construct the enemy status, resulting in more dynamic enemies. We use the threat value as an input parameter calculated from the enemies’ stats in every platform, such as total damage that the enemy might produce, the player’s health point, and the enemy’s movement speed. We conclude that using a classical genetic algorithm may produce dynamic enemy status through the desired threat or danger set by the game designer as an input parameter. Moreover, the game designer may limit the generation with constraints.
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- K. Salen and E. Zimmerman, Rules of Play: Game Design Fundamentals. The MIT Press, 2003.
- N. Peever, D. Johnson, and J. Gardner, “Personality & Video Game Genre Preferences,” in Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System, in IE ‘12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2336727.2336747
- L. Husniah, F. Fannani, A. S. Kholimi, and A. E. Kristanto, “Game Development to Introduce Indonesian Traditional Weapons using MDA Framework,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 27–36, Nov. 2018. https://doi.org/10.22219/kinetik.v4i1.713
- Entertainment Software Association (ESA), “2022 essential facts About the video game industry,” Entertainment Software Association. 2022.
- Valve, “Steam Store,” Apr. 09, 2023.
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- A. B. Harisa, H. Haryanto, and H. A. Santoso, “Model Tingkat Kesulitan Dinamis berbasis Logika Fuzzy pada Game Wayang Ramayana,” in SEMNASTEKNOMEDIA ONLINE, 2016, pp. 2–6.
- A. Gellel and P. Sweetser, “A Hybrid Approach to Procedural Generation of Roguelike Video Game Levels,” in Proceedings of the 15th International Conference on the Foundations of Digital Games, in FDG ‘20. New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3402942.3402945
- G. N. Yannakakis and J. Togelius, “Experience-Driven Procedural Content Generation,” IEEE Trans Affect Comput, vol. 2, no. 3, pp. 147–161, Jul. 2011. https://doi.org/10.1109/T-AFFC.2011.6
- M. Kaidan, C. Y. Chu, T. Harada, and R. Thawonmas, “Procedural generation of angry birds levels that adapt to the player’s skills using genetic algorithm,” in 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), Oct. 2015, pp. 535–536. https://doi.org/10.1109/GCCE.2015.7398674
- A. Sarkar, Z. Yang, and S. Cooper, “Controllable level blending between games using variational autoencoders,” arXiv preprint arXiv:2002.11869, 2020. https://doi.org/10.48550/arXiv.2002.11869
- S. M. Lucas and V. Volz, “Tile Pattern KL-Divergence for Analysing and Evolving Game Levels,” in Proceedings of the Genetic and Evolutionary Computation Conference, in GECCO ‘19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 170–178. https://doi.org/10.1145/3321707.3321781
- R. A. Pambudi, W. Lubis, F. R. Saputra, H. P. Maulidina, and V. N. Wijayaningrum, “Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, no. 4, pp. 297–304, Oct. 2019. https://doi.org/10.22219/kinetik.v4i4.859
- Nery Bandeira, I. et al. (2022). Dynamic Difficulty Adjustment in Digital Games: Comparative Study Between Two Algorithms Using Electrodermal Activity Data. In: Fang, X. (eds) HCI in Games. HCII 2022. Lecture Notes in Computer Science, vol 13334. Springer, Cham. https://doi.org/10.1007/978-3-031-05637-6_5
- J. Pfau, J. D. Smeddinck, and R. Malaka, “Enemy Within: Long-Term Motivation Effects of Deep Player Behavior Models for Dynamic Difficulty Adjustment,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, in CHI ‘20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 1–10. https://doi.org/10.1145/3313831.3376423
- M. Zohaib, “Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review,” Advances in Human-Computer Interaction, vol. 2018, p. 5681652, Nov. 2018. https://doi.org/10.1155/2018/5681652
- A. B. Harisa and W. K. Tai, “Pacing-based Procedural Dungeon Level Generation: Alternating Level Creation to Meet Designer’s Expectations,” International Journal of Computing and Digital Systems, vol. 12, no. 1, pp. 401–416, 2022. https://doi.org/10.12785/ijcds/120132
- B. M. F. Viana and S. R. dos Santos, “A Survey of Procedural Dungeon Generation,” in 2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), 2019, pp. 29–38. https://doi.org/10.1109/SBGames.2019.00015
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- B. Piller, C. Johanson, C. Phillips, C. Gutwin, and R. L. Mandryk, “Is a Change as Good as a Rest? Comparing BreakTypes for Spaced Practice in a Platformer Game,” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play, New York, NY, USA: ACM, Nov. 2020, pp. 294–305. https://doi.org/10.1145/3410404.3414225
References
K. Salen and E. Zimmerman, Rules of Play: Game Design Fundamentals. The MIT Press, 2003.
N. Peever, D. Johnson, and J. Gardner, “Personality & Video Game Genre Preferences,” in Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System, in IE ‘12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2336727.2336747
L. Husniah, F. Fannani, A. S. Kholimi, and A. E. Kristanto, “Game Development to Introduce Indonesian Traditional Weapons using MDA Framework,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 27–36, Nov. 2018. https://doi.org/10.22219/kinetik.v4i1.713
Entertainment Software Association (ESA), “2022 essential facts About the video game industry,” Entertainment Software Association. 2022.
Valve, “Steam Store,” Apr. 09, 2023.
T. H. Apperley, “Genre and game studies: Toward a critical approach to video game genres,” Simul Gaming, vol. 37, no. 1, pp. 6–23, Mar. 2006. https://doi.org/10.1177/1046878105282278
E. F. Melcer and M. A. M. Cuerdo, “Death and rebirth in platformer games,” Game User Experience And Player-Centered Design, pp. 265–293, 2020. https://doi.org/10.1007/978-3-030-37643-7_12
A. B. Harisa, H. Haryanto, and H. A. Santoso, “Model Tingkat Kesulitan Dinamis berbasis Logika Fuzzy pada Game Wayang Ramayana,” in SEMNASTEKNOMEDIA ONLINE, 2016, pp. 2–6.
A. Gellel and P. Sweetser, “A Hybrid Approach to Procedural Generation of Roguelike Video Game Levels,” in Proceedings of the 15th International Conference on the Foundations of Digital Games, in FDG ‘20. New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3402942.3402945
G. N. Yannakakis and J. Togelius, “Experience-Driven Procedural Content Generation,” IEEE Trans Affect Comput, vol. 2, no. 3, pp. 147–161, Jul. 2011. https://doi.org/10.1109/T-AFFC.2011.6
M. Kaidan, C. Y. Chu, T. Harada, and R. Thawonmas, “Procedural generation of angry birds levels that adapt to the player’s skills using genetic algorithm,” in 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), Oct. 2015, pp. 535–536. https://doi.org/10.1109/GCCE.2015.7398674
A. Sarkar, Z. Yang, and S. Cooper, “Controllable level blending between games using variational autoencoders,” arXiv preprint arXiv:2002.11869, 2020. https://doi.org/10.48550/arXiv.2002.11869
S. M. Lucas and V. Volz, “Tile Pattern KL-Divergence for Analysing and Evolving Game Levels,” in Proceedings of the Genetic and Evolutionary Computation Conference, in GECCO ‘19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 170–178. https://doi.org/10.1145/3321707.3321781
R. A. Pambudi, W. Lubis, F. R. Saputra, H. P. Maulidina, and V. N. Wijayaningrum, “Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, no. 4, pp. 297–304, Oct. 2019. https://doi.org/10.22219/kinetik.v4i4.859
Nery Bandeira, I. et al. (2022). Dynamic Difficulty Adjustment in Digital Games: Comparative Study Between Two Algorithms Using Electrodermal Activity Data. In: Fang, X. (eds) HCI in Games. HCII 2022. Lecture Notes in Computer Science, vol 13334. Springer, Cham. https://doi.org/10.1007/978-3-031-05637-6_5
J. Pfau, J. D. Smeddinck, and R. Malaka, “Enemy Within: Long-Term Motivation Effects of Deep Player Behavior Models for Dynamic Difficulty Adjustment,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, in CHI ‘20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 1–10. https://doi.org/10.1145/3313831.3376423
M. Zohaib, “Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review,” Advances in Human-Computer Interaction, vol. 2018, p. 5681652, Nov. 2018. https://doi.org/10.1155/2018/5681652
A. B. Harisa and W. K. Tai, “Pacing-based Procedural Dungeon Level Generation: Alternating Level Creation to Meet Designer’s Expectations,” International Journal of Computing and Digital Systems, vol. 12, no. 1, pp. 401–416, 2022. https://doi.org/10.12785/ijcds/120132
B. M. F. Viana and S. R. dos Santos, “A Survey of Procedural Dungeon Generation,” in 2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), 2019, pp. 29–38. https://doi.org/10.1109/SBGames.2019.00015
A. B. Moghadam and M. K. Rafsanjani, “A genetic approach in procedural content generation for platformer games level creation,” in 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 2017, pp. 141–146. https://doi.org/10.1109/CSIEC.2017.7940160
A. Sarkar, R. Padte, J. Cao, and S. Cooper, “Desire Path-Inspired Procedural Placement of Coins in a Platformer Game,” in Joint Proceedings of the AIIDE 2018 Workshops co-located with 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2018), Edmonton, Canada, November 13-14, 2018, J. Zhu, Ed., in CEUR Workshop Proceedings, vol. 2282. CEUR-WS.org, 2018.
A. Summerville and M. Mateas, “Super Mario as a String: Platformer Level Generation Via LSTMs,” CoRR, vol. abs/1603.00930, 2016. https://doi.org/10.48550/arXiv.1603.00930
B. Piller, C. Johanson, C. Phillips, C. Gutwin, and R. L. Mandryk, “Is a Change as Good as a Rest? Comparing BreakTypes for Spaced Practice in a Platformer Game,” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play, New York, NY, USA: ACM, Nov. 2020, pp. 294–305. https://doi.org/10.1145/3410404.3414225