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An Interactive Multi-Agent System for Game Design

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The Computer Games Journal

Abstract

This paper presents a novel approach to procedural generation of game maps for multi-player, competitive video games. A multi-agent evolutionary system is employed to place streets, buildings and other items, resulting in a playable video game map. The system utilises computational agents that act in conjunction with the human designer to produce maps that exhibit desirable characteristics. This paper compares the impact that the additional agents have in terms of the quality of candidate solutions. The results indicate that the use of the agents produces higher quality solutions in comparison to a traditional interactive genetic algorithm.

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References

  • Amato, A. (2017). Procedural content generation in the game industry. In O. Korn & N. Lee (Eds.), Game Dynamics: Best Practices in Procedural and Dynamic Game Content Generation. Cham: Springer International Publishing.

    Google Scholar 

  • Cardamone, L., Loiacono, D., & Lanzi, P. L. (2011). Interactive Evolution for the Procedural Generation of Tracks in a High-End Racing Game. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. Dublin, Ireland: ACM.

  • Cardamone, L., Yannakakis, G. N., Togelius, J., & Lanzi, P. L. (2001). Evolving interesting maps for a first person shooter. In European Conference on the Applications of Evolutionary Computation. Springer 2011b, 63–72.

  • Cifaldi, F. (2006). Analysts: FPS ‘Most Attractive’ Genre for Publishers. http://www.gamasutra.com/php-bin/news_index.php?story=8241

  • Connor, A. M., Greig, T. J., & Kruse, J. (2017). Evaluating the impact of procedurally generated content on game immersion. The Computer Games Journal, 6(4), 209–225.

    Article  Google Scholar 

  • Cook, M., & Colton, S. (2014). Ludus ex machina: Building a 3D game designer that competes alongside humans. In Proceedings of the 5th international conference on computational creativity.

  • De Carli, D. M., Bevilacqua, F., Tadeu Pozzer, C., & d’Ornellas, M. C. (2011). A Survey of Procedural Content Generation Techniques Suitable to Game Development. In 2011 Brazilian Symposium on Games and Digital Entertainment Presented at the 2011 Brazilian Symposium on Games and Digital Entertainment 26–35 https://doi.org/10.1109/SBGAMES.2011.15

  • Doran, J., & Parberry, I. (2010). Controlled procedural terrain generation using software agents. IEEE Transactions on Computational Intelligence and AI in Games, 2(2), 111–119.

    Article  Google Scholar 

  • Hendrikx, M., Meijer, S., Van Der Velden, J., & Iosup, A. (2013). Procedural content generation for games: A survey. ACM Transactions on Multimedia Computing Communications, and Applications, 9(1), 1–22.

    Article  Google Scholar 

  • Hullett, K., & Whitehead, J. (2010). Design patterns in FPS levels. In Proceedings of the Fifth International Conference on the Foundations of Digital Games. Monterey, California: ACM.

  • Järvinen, A. (2009). Games without frontiers: Methods for game studies and design. Tampere, Finland: VDM, Verlag Dr. Müller.

    Google Scholar 

  • Kosorukoff, A. (2001). Human based genetic algorithm. In Systems, Man, and Cybernetics, 2001 IEEE International Conference on, 2001 IEEE 3464–3469.

  • Koster, R. (2018, January 17). The cost of games. Gamasutra. https://www.gamasutra.com/blogs/RaphKoster/20180117/313211/The_cost_of_games.php. Accessed 23 February 2019

  • Kruse, J., & Connor, A. M. (2015). Multi-agent evolutionary systems for the generation of complex virtual worlds. EAI Endorsed Transactions on Creative Technologies, 2(5), e5.

    Article  Google Scholar 

  • Kruse, J. (2019). Designer-driven Procedural Game Content Generation using Multi-agent Evolutionary Computation (Thesis). Auckland University of Technology. Retrieved from https://openrepository.aut.ac.nz/handle/10292/12944

  • Kruse, J., Sosa, R., & Connor, A. M. (2016). Procedural urban environments for FPS games. In Proceedings of the Australasian computer science week multiconference 1–5.

  • Lanzi, P. L., Lomacono, D., & Stucchi, R. (2014). Evolving maps for match balancing in first person shooters. In 2014 IEEE Conference on Computational Intelligence and Games, 2014 IEEE 1–8.

  • Liapis, A., Yannakakis, G. N., & Togelius, J. (2014). Computational game creativity. In Proceedings of the fifth international conference on computational creativity.

  • 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.

    Article  Google Scholar 

  • Mark, B., Berechet, T., Mahlmann, T., & Togelius, J. (2015). Procedural Generation of 3D Caves for Games on the GPU. In 10th International Conference on the Foundations of Digital Games. Pacific Grove, CA.

  • Mawhorter, P., & Mateas, M. (2010). Procedural level generation using occupancy-regulated extension. In 2010 IEEE Symposium on Computational Intelligence and Games (CIG), 2010 IEEE 351–358

  • Museth, K. (2014). Hierarchical digital differential analyzer for efficient ray-marching in openvdb. In ACM SIGGRAPH 2014 Talks ACM 40.

  • Pinelle, D., Wong, N., & Stach, T. (2008). Heuristic evaluation for games: usability principles for video game design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1453–1462). ACM. http://dl.acm.org/citation.cfm?id=1357282

  • Predescu, A., & Mocanu, M. (2020). A data driven survey of video games. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) 1–6.

  • Provano, M., Mannetti, R., & Lomacono, D. (2015). Volcano: An interactive sword generator. In Games Entertainment Media Conference (GEM), 2015 IEEE, 1–8.

  • Short, T., & Adams, T. (2017). Procedural generation in game design. CRC Press.

  • Smith, A. M., Andersen, E., Mateas, M., & Popović, Z. (2012). A case study of expressively constrainable level design automation tools for a puzzle game. In Proceedings of the International Conference on the Foundations of Digital Games 156–163.

  • Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. In Proceedings of the IEEE 89: 1275–1296.

  • Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., Yannakakis, G. N., & Grappiolo, C. (2013). Controllable procedural map generation via multiobjective evolution. Genetic Programming and Evolvable Machines, 14(2), 245–277.

    Article  Google Scholar 

  • Togelius, J., Yannakakis, G. N., Stanley, K. O., & Browne, C. (2011). Search-based procedural content generation: A taxonomy and survey. IEEE Transactions on Computational Intelligence and AI in Games, 3(3), 172–186.

    Article  Google Scholar 

  • Togelius, J., Preuss, M., & Yannakakis, G. N. (2010). Towards multiobjective procedural map generation. In Proceedings of the 2010 workshop on procedural content generation in games 1–8.

  • Togelius, J., Yannakakis, G. N., Stanley, K. O., & Browne, C. (2010). Search-based procedural content generation. In European Conference on the Applications of Evolutionary Computation 141–150 Springer.

  • Walsh, P., & Gade, P. (2010). Terrain generation using an interactive genetic algorithm. In Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010 (pp. 1–7). IEEE.

  • Wright, T., Boria, E., & Breidenbach, P. (2002). Creative player actions in FPS online video games: Playing Counter-Strike. Game studies, 2(2), 103–123.

    Google Scholar 

  • Yannakakis, G. N., & Togelius, J. (2011). Experience-driven procedural content generation. IEEE Transactions on Affective Computing, 2(3), 147–161.

    Article  Google Scholar 

  • Yannakakis, G. N., & Togelius, J. (2018). Generating content. In G. N. Yannakakis & J. Togelius (Eds.), Artificial Intelligence and Games. Cham: Springer.

    Chapter  Google Scholar 

  • Yoon, D., & Kim, K.-J. (2012). 3D game model and texture generation using interactive genetic algorithm. In Proceedings of the Workshop at SIGGRAPH Asia 53–58

  • Zhu, M., Zhao, F., Fang, X., & Moser, C. (2017). Developing playability heuristics based on nouns and adjectives from online game reviews. International Journal of Human-Computer Interaction, 33(3), 241–253. https://doi.org/10.1080/10447318.2016.1240283.

    Article  Google Scholar 

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Correspondence to A. M. Connor.

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This research outlined in this has been approved by the ethics committee (AUTEC), reference 17/80.

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Kruse, J., Connor, A.M. & Marks, S. An Interactive Multi-Agent System for Game Design. Comput Game J 10, 41–63 (2021). https://doi.org/10.1007/s40869-020-00119-z

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