Skip to main content
Springer Nature Link
Log in

Procedural content improvement of game bosses with an evolutionary algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

We present our Evolutionary Boss Improvement (EBI) approach, which receives partially complete bosses as input and generates fully equipped bosses that are complete. Additionally, the evolutionary algorithm and the new genetic operations included in EBI favor genetic improvement, which affects the initial partial content of the incomplete bosses originally provided. We evaluate our approach using Kromaia, a commercial video game released on PlayStation 4 and PC. EBI uses an evolutionary algorithm to evolve a population of bosses guided by duels between the bosses being generated and a simulated player. Our approach evaluates the quality, in terms of game experience, of both the bosses generated and those included in Kromaia using six metrics (Completion, Duration, Uncertainty, Killer Moves, Permanence, and Lead Change) from the literature. The results show that the quality of the bosses created by EBI is comparable to the quality of the original bosses that were manually created by the developers of Kromaia. However, the EBI approach reduces the time required to build the bosses from five months (of elapsed time as opposed to dedicated time) to just 100 minutes of unattended run. EBI enables developers to accelerate the creation of content, such as bosses, which is essential to ensure player engagement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. This includes testing with real players.

References

  1. Althöfer I (2003) Computer-aided game inventing. Technical Report, Friedrich Schiller Universität Jena. https://www.minet.uni-jena.de/preprints/althoefer_03/CAGI.pdf. Accessed June 2021

  2. Arcuri A, Briand L (2014) A hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Softw Test Verif Reliab 24 (3):219–250. https://doi.org/10.1002/stvr.1486

    Article  Google Scholar 

  3. Arcuri A, Fraser G (2013) Parameter tuning or default values? an empirical investigation in search-based software engineering. Empir Softw Eng 18 (3):594–623. https://doi.org/10.1007/s10664-013-9249-9

    Article  Google Scholar 

  4. Ashley DR, Chockalingam V, Kuzma B, Bulitko V (2019) Learning to select mates in evolving non-playable characters. In: 2019 IEEE Conference on Games (CoG), pp 1–8

  5. Beyer M, Agureikin A, Anokhin A, Laenger C, Nolte F, Winterberg J, Renka M, Rieger M, Pflanzl N, Preuss M et al (2016) An integrated process for game balancing. In: 2016 IEEE Conference on computational intelligence and games (CIG), IEEE, pp 1-8

  6. Bhatt A, Lee S, de Mesentier Silva F, Watson CW, Togelius J, Hoover AK (2018) Exploring the hearthstone deck space. In: Proceedings of the 13th international conference on the foundations of digital games, pp 1–10

  7. Blasco D, Cetina C, Pastor O (2020) A fine-grained requirement traceability evolutionary algorithm: kromaia, a commercial video game case study. Inf Softw Technol 119. https://doi.org/10.1016/j.infsof.2019.106235https://doi.org/10.1016/j.infsof.2019.106235

  8. Blasco D, Font J, Zamorano M, Cetina C (2021) An evolutionary approach for generating software models: The case of Kromaia in game software engineering. J Syst Softw 171:110804. https://doi.org/10.1016/j.jss.2020.110804https://doi.org/10.1016/j.jss.2020.110804. http://www.sciencedirect.com/science/article/pii/S0164121220302089

    Article  Google Scholar 

  9. Boussaïd I, Siarry P, Ahmed-Nacer M (2017) A survey on search-based model-driven engineering. Autom Softw Eng 24(2):233–294

    Article  Google Scholar 

  10. Brown JA, Ashlock D, Houghten S, Romualdo A (2020) Evolutionary graph compression and diffusion methods for city discovery in role playing games. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp 1–8. https://doi.org/10.1109/CEC48606.2020.9185601

  11. Browne C (2005) Connection games: Variations on a theme AK peters. Natick, Massachussetts

    MATH  Google Scholar 

  12. Browne C, Maire F (2010) Evolutionary game design. IEEE Trans Comput Intellig and AI in Games 2(1):1–16. https://doi.org/10.1109/TCIAIG.2010.2041928

    Article  Google Scholar 

  13. Cardamone L, Yannakakis GN, Togelius J, Lanzi PL (2011) Evolving interesting maps for a first person shooter. In: European conference on the applications of evolutionary computation, Springer, pp 63–72

  14. De Oliveira Barros M, Dias-Neto AC (2011) 0006/2011-threats to validity in search-based software engineering empirical studies. RelaTe-DIA 5(1)

  15. de Mesentier Silva F, Lee S, Togelius J, Nealen A (2017) Ai as evaluator: search driven playtesting of modern board games. In: AAAI Workshops

  16. de Mesentier Silva F, Canaan R, Lee S, Fontaine MC, Togelius J, Hoover AK (2019) Evolving the hearthstone meta. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8. Accessed June 2021

  17. Delarosa O, Dong H, Ruan M, Khalifa A, Togelius J (2021) Mixed-initiative level design with rl brush. In: International conference on computational intelligence in music, sound, art and design (Part of EvoStar), Springer, pp 412–426

  18. Eiben AE, Smith JE et al (2003) Introduction to evolutionary computing, vol 53. Springer

  19. Games E (1998) Unreal engine, version 2018.3.9. http://www.unrealengine.com/

  20. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inform Sci 180(10):2044–2064. https://doi.org/10.1016/j.ins.2009.12.010. https://www.sciencedirect.com/science/article/pii/S0020025509005404, special Issue on Intelligent Distributed Information Systems

    Article  Google Scholar 

  21. Guzdial M, Liao N, Riedl M (2018) Co-creative level design via machine learning. arXiv:180909420

  22. Ha D, Eck D (2017) A neural representation of sketch drawings. arXiv:170403477

  23. Hendrikx M, Meijer S, Van Der Velden J, Iosup A (2013) Procedural content generation for games: a survey. ACM Trans Multimedia Comput Commun Appl 9(1). https://doi.org/10.1145/2422956.2422957

  24. Iida H, Takahara K, Nagashima J, Kajihara Y, Hashimoto T (2004) An application of game-refinement theory to mah jong. In: Entertainment Computing - ICEC 2004, Third International Conference, Eindhoven, The Netherlands, September 1-3, 2004, Proceedings, pp 333–338. https://doi.org/10.1007/978-3-540-28643-1_41

  25. Jaffe A, Miller A, Andersen E, Liu YE, Karlin A, Popovic Z (2012) Evaluating competitive game balance with restricted play. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol 8

  26. Karavolos D, Liapis A, Yannakakis GN (2018) Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model. In: Proceedings of the 13th international conference on the Foundations of digital games, pp 1–10

  27. Kent S (2002) Model driven engineering. In: Butler M, Petre L, Sere K (eds) Integrated Formal Methods. Springer, Berlin, pp 286–298

  28. Kent S (2002) Model driven engineering. In: International conference on integrated formal methods, Springer, pp 286–298. Accessed June 2021

  29. Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat comput 4(2):87–112

    Article  Google Scholar 

  30. Kramer W (2000) What makes a game good? The Games Journal. http://www.thegamesjournal.com/articles/WhatMakesaGame.shtml

  31. Langdon WB (2015) Genetically improved software. Springer International Publishing, Cham, pp 181–220. https://doi.org/10.1007/978-3-319-20883-1_8

    Google Scholar 

  32. Lanzi PL, Loiacono D, Stucchi R (2014) Evolving maps for match balancing in first person shooters. In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–8

  33. Liapis A, Yannakakis GN, Togelius J (2013) Sentient sketchbook: computer-aided game level authoring. In: Proceedings of ACM Conference on Foundations of Digital Games. In Print

  34. Liapis A, Yannakakis GN, Togelius J (2013) Sentient world: Human-based procedural cartography. In: International conference on evolutionary and biologically inspired music and art, Springer, pp 180–191

  35. Lim SM, Sultan ABM, Sulaiman MN, Mustapha A, Leong KY (2017) Crossover and mutation operators of genetic algorithms. Int J Mach Learn Comput 7(1):9–12

    Article  Google Scholar 

  36. Liu J, Snodgrass S, Khalifa A, Risi S, Yannakakis GN, Togelius J (2021) Deep learning for procedural content generation. Neural Comput Appl 33(1):19–37

    Article  Google Scholar 

  37. Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730–3738

  38. Loiacono D, Arnaboldi L (2017) Fight or flight: evolving maps for cube 2 to foster a fleeing behavior. In: 2017 IEEE conference on computational intelligence and games (CIG), pp 199–206. https://doi.org/10.1109/CIG.2017.8080436

  39. Loiacono D, Arnaboldi L (2019) Multiobjective evolutionary map design for cube 2: Sauerbraten. IEEE Trans Games 11(1):36–47. https://doi.org/10.1109/TG.2018.2830746

    Article  Google Scholar 

  40. Ølsted PT, Ma B, Risi S (2015) Interactive evolution of levels for a competitive multiplayer fps. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp 1527–1534. https://doi.org/10.1109/CEC.2015.7257069

  41. Pantaleev A (2012) In search of patterns: Disrupting rpg classes through procedural content generation. In: Proceedings of the The third workshop on procedural content generation in games, pp 1–5

  42. Park K, Mott BW, Min W, Boyer KE, Wiebe EN, Lester JC (2019) Generating educational game levels with multistep deep convolutional generative adversarial networks. In: 2019 IEEE Conference on Games (CoG), IEEE, pp 1–8

  43. Pavai G, Geetha T (2016) A survey on crossover operators. ACM Computing Surveys (CSUR) 49(4):1–43

    Article  Google Scholar 

  44. Pérez F, Ziadi T, Cetina C (2020) Utilizing automatic query reformulations as genetic operations to improve feature location in software models. IEEE Transactions on Software Engineering

  45. Petke J, Haraldsson SO, Harman M, Langdon WB, White DR, Woodward JR (2018) Genetic improvement of software: a comprehensive survey. IEEE Trans Evol Comput 22(3):415–432. https://doi.org/10.1109/TEVC.2017.2693219

    Article  Google Scholar 

  46. Pfau J, Liapis A, Volkmar G, Yannakakis GN, Malaka R (2020) Dungeons & replicants: automated game balancing via deep player behavior modeling. In: 2020 IEEE Conference on Games (CoG), IEEE, pp 431–438

  47. Reyno E M, Carsí Cubel JÁ (2009) Automatic prototyping in model-driven game development. Computers in Entertainment (CIE) 7(2):1–9

    Article  Google Scholar 

  48. Ruela A, Guimarães FG (2017) Procedural generation of non-player characters in massively multiplayer online strategy games. Soft Comput 21(23):7005–7020. https://doi.org/10.1007/s00500-016-2238-3

    Article  Google Scholar 

  49. Sarkar A, Cooper S (2018) Blending levels from different games using lstms. In: AIIDE Workshops

  50. Sarkar A, Yang Z, Cooper S (2020) Controllable level blending between games using variational autoencoders. arXiv:200211869

  51. Serpa YR, Rodrigues MAF (2019) Towards machine-learning assisted asset generation for games: a study on pixel art sprite sheets. In: 2019 18th brazilian symposium on computer games and digital entertainment, SBGames, IEEE, pp 182–191

  52. Shaker N, Togelius J, Nelson MJ (2016) Procedural content generation in games. Springer

  53. Siu K, Butler E, Zook A (2016) A programming model for boss encounters in 2d action games. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol 12

  54. Snodgrass S, Sarkar A (2020) Multi-domain level generation and blending with sketches via example-driven bsp and variational autoencoders. In: International Conference on the Foundations of Digital Games, pp 1–11

  55. Summerville A, Snodgrass S, Guzdial M, Holmgård C, Hoover AK, Isaksen A, Nealen A, Togelius J (2018) Procedural content generation via machine learning (PCGML). IEEE Trans Games 10(3):257–270. https://doi.org/10.1109/TG.2018.2846639

    Article  Google Scholar 

  56. Tang S, Hanneghan M (2010) A model-driven framework to support development of serious games for game-based learning. In: 2010 Developments in E-systems Engineering, IEEE, pp 95–100

  57. Thompson JM (2000) Defining the abstract. The Games Journal. http://www.thegamesjournal.com/articles/DefiningtheAbstract.shtml. Accessed June 2021

  58. Togelius J, Yannakakis GN, Stanley KO, Browne C (2011) Search-based procedural content generation: a taxonomy and survey. IEEE Trans Comput Intellig and AI in Games 3(3):172–186. http://dblp.uni-trier.de/db/journals/tciaig/tciaig3.html#TogeliusYSB11

    Article  Google Scholar 

  59. Vargha A, Delaney HD (2000) A critique and improvement of the cl common language effect size statistics of mcgraw and wong. J Educ Behav Stat 25(2):101–132. https://doi.org/10.3102/10769986025002101. http://jeb.sagepub.com/content/25/2/101.abstract

    Google Scholar 

  60. van der Ven JS, Jansen AGJ, Nijhuis JAG, Bosch J (2006) Design decisions: The bridge between rationale and architecture. pp 4–5. https://doi.org/10.1007/978-3-540-30998-7_16

  61. Volz V, Rudolph G, Naujoks B (2016) Demonstrating the feasibility of automatic game balancing. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016. pp 269–276

  62. Yannakakis GN, Togelius J (2018) Artificial intelligence and games, vol 2. Springer

  63. Yannakakis GN, Liapis A, Alexopoulos C (2014) Mixed-initiative co-creativity

  64. Yoo B, Kim KJ (2016) Changing video game graphic styles using neural algorithms. In: 2016 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp 1–2

Download references

Funding

This work was supported in part by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF funds under the Project VARIATIVA under Grant PID2021-128695OB-I00, and in part by the Gobierno de Aragón (Spain) (Research Group S05_20D).

Author information

Authors and Affiliations

  1. SVIT Research Group, Universidad San Jorge, Zaragoza, Spain

    Daniel Blasco, Jaime Font, Francisca Pérez & Carlos Cetina

Authors
  1. Daniel Blasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaime Font
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisca Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Cetina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisca Pérez.

Ethics declarations

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blasco, D., Font, J., Pérez, F. et al. Procedural content improvement of game bosses with an evolutionary algorithm. Multimed Tools Appl 82, 10277–10309 (2023). https://doi.org/10.1007/s11042-022-13674-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13674-6

Keywords

Search

Navigation