Abstract
This paper introduces an evolutionary method for generating levels for adventure games, combining speed, guaranteed solvability of levels and authorial control. For this purpose, a new graph-based two-phase level encoding scheme is developed. This method encodes the structure of the level as well as its contents into two abstraction layers: the higher level defines an abstract representation of the game level and the distribution of its content among different inter-connected game zones. The lower level describes the content of each game zone as a set of graphs containing rooms, doors, monsters, keys and treasure chests. Using this representation, game worlds are encoded as individuals in an evolutionary algorithm and evolved according to an evaluation function meant to approximate the entertainment provided by the game level. The algorithm is implemented into a design tool that can be used by game designers to specify several constraints of the worlds to be generated. This tool could be used to facilitate the design of game levels, for example to make professional-level content production possible for non-experts.
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References
Adams, E., Dormans, J.: Game Mechanics: Advanced Game Design. New Riders, San Francisco (2012)
Izquierdo, R.: GraphQuest (2015). http://robertoia.github.io/GraphQuest/
Couchet, J., Manrique, D., Porras, L.: Grammar-guided neural architecture evolution. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 437–446. Springer, Heidelberg (2007)
Dormans, J.: Adventures in level design: generating missions and spaces for action adventure games. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games (2010)
Font, J.M., Manrique, D., Pascua, E.: Grammar-guided evolutionary construction of bayesian networks. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 60–69. Springer, Heidelberg (2011)
Karavolos, D., Anders, B., Bidarra, R.: Mixed-initiative design of game levels: integrating mission and space into level generation. In: Proceedings of the 10th International Conference on the Foundations of Digital Games (2015)
Kerssemakers, M., Tuxen, J., Togelius, J., Yannakakis, G.N.: A procedural procedural level generator generator. In: IEEE Conference on Computational Intelligence and Games, CIG 2012, Granada, pp. 335–341 (2012)
Koster, R.: Theory of Fun for Game Design. O’Reilly Media Inc, California (2013)
Liapis, A., Yannakakis, G.N., Togelius, J.: Sentient sketchbook: computer-aided game level authoring. In: Proceedings of ACM Conference on Foundations of Digital Games (2013)
van der Linden, R., Lopes, R., Bidarra, R.: Designing procedurally generated levels. In: Ninth Artificial Intelligence and Interactive Digital Entertainment Conference (2013)
Myerson, R.: Game Theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)
Nintendo: The Legend of Zelda: Oracle of Seasons (2001)
Ochoa, G.: On genetic algorithms and Lindenmayer systems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 335–344. Springer, Heidelberg (1998)
Preuss, M., Liapis, A., Togelius, J.: Searching for good and diverse game levels. In: 2014 IEEE Conference on Computational Intelligence and Games, CIG 2014, Dortmund, pp. 1–8 (2014)
Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer Science and Business Media, Chicago (1990)
Shaker, N., Togelius, J., Nelson, M.J.: Procedural Content Generation in Games: A Textbook and an Overview of Current Research. Springer, New York (2015)
Shaker, N., Nicolau, M., Yannakakis, G.N., Togelius, J., O’ Neill, M.: Evolving levels for super mario bros using grammatical evolution. In: IEEE Conference Computational Intelligence and Games (CIG), pp. 304–311 (2012)
Smith, A.M., Mateas, M.: Answer set programming for procedural content generation: a design space approach. In: IEEE Transactions onComputational Intelligence and AI in Games, vol. 3, pp. 187-200(2011)
Sorenson, N., Pasquier, P.: Towards a generic framework for automated video game level creation. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 131–140. Springer, Heidelberg (2010)
Sorenson, N., Pasquier, P., DiPaola, S.: A generic approach to challenge modeling for the procedural creation of video game levels. IEEE Trans. Comput. Intell. AI Games 3, 229–244 (2011)
Togelius, J., Yannakakis, G.N., Stanley, K.O., Browne, C.: Search-based procedural content generation: a taxonomy and survey. IEEE Trans. Comput. Intell. AI Games 1, 172–186 (2011)
Togelius, J., De Nardi, R., Lucas, S.M.: Towards automatic personalised content creation for racing games. In: IEEE Symposium on Computational Intelligence and Games, CIG 2007, Honolulu, pp. 252–259 (2007)
Whigham, P.A.: Grammatically-based genetic programming. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 33–41 (1995)
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Font, J.M., Izquierdo, R., Manrique, D., Togelius, J. (2016). Constrained Level Generation Through Grammar-Based Evolutionary Algorithms. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_36
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DOI: https://doi.org/10.1007/978-3-319-31204-0_36
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