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Learning from Play: Facilitating Character Design Through Genetic Programming and Human Mimicry

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

Abstract

Mimicry and play are fundamental learning processes by which individuals can acquire behaviours, skills and norms. In this paper we utilise these two processes to create new game characters by mimicking and learning from actual human players. We present our approach towards aiding the design process of game characters through the use of genetic programming. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. Computational creativity approaches this issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks. Our GP approach to this problem not only mimics actual human play but creates character controllers which can be further authored and developed by a designer. This keeps the designer in the loop while reducing repetitive labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework and preliminary results supporting our claim.

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References

  1. Bryson, J.J., Stein, L.A.: Modularity and design in reactive intelligence. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 1115–1120. Morgan Kaufmann, Seattle, August 2001

    Google Scholar 

  2. Champandard, A.J.: AI Game Development. New Riders Publishing (2003)

    Google Scholar 

  3. Gaudl, S.E., Davies, S., Bryson, J.J.: Behaviour oriented design for real-time-strategy games - an approach on iterative development for starcraft ai. In: Proceedings of the Foundations of Digital Games, pp. 198–205. Society for the Advancement of Science of Digital Games (2013)

    Google Scholar 

  4. Holmgard, C., Liapis, A., Togelius, J., Yannakakis, G.: Evolving personas for player decision modeling. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8, August 2014

    Google Scholar 

  5. Krawiec, K., O’Reilly, U.M.: Behavioral programming: a broader and more detailed take on semantic gp. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 935–942. ACM (2014)

    Google Scholar 

  6. Meffert, K., Rotstan, N., Knowles, C., Sangiorgi, U.: Jgap-java genetic algorithms and genetic programming package, September 2000. http://jgap.sf.net (last viewed: January 2015)

  7. Ortega, J., Shaker, N., Togelius, J., Yannakakis, G.N.: Imitating human playing styles in super mario bros. Entertainment Computing 4(2), 93–104 (2013)

    Article  Google Scholar 

  8. Osborn, J.C., Mateas, M.: A game-independent play trace dissimilarity metric. In: Proceedings of the Foundations of Digital Games. Society for the Advancement of Science of Digital Games (2014)

    Google Scholar 

  9. Perez, D., Nicolau, M., O’Neill, M., Brabazon, A.: Evolving behaviour trees for the mario ai competition using grammatical evolution. In: Di Chio, C., et al. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 123–132. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: A field guide to genetic programming. Lulu. com (2008)

    Google Scholar 

  11. Schwefel, H.P.P.: Evolution and optimum seeking: the sixth generation. John Wiley & Sons, Inc. (1993)

    Google Scholar 

  12. Smit, S.K., Eiben, A.E.: Comparing parameter tuning methods for evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 399–406. IEEE (2009)

    Google Scholar 

  13. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10, 99–127 (2002)

    Article  Google Scholar 

  14. Togelius, J., Karakovskiy, S., Baumgarten, R.: The 2009 mario ai competition. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  15. Togelius, J., Yannakakis, G., Karakovskiy, S., Shaker, N.: Assessing believability. In: Hingston, P. (ed.) Believable Bots, pp. 215–230. Springer, Heidelberg (2012)

    Google Scholar 

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Correspondence to Swen E. Gaudl .

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Gaudl, S.E., Osborn, J.C., Bryson, J.J. (2015). Learning from Play: Facilitating Character Design Through Genetic Programming and Human Mimicry. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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