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Quest reward optimization method for massive multiplayer online role playing games

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Abstract

We propose a parameter optimization method for quest design of massive multiplayer online role playing games (MMORPGs). Our system consists of two techniques—selective sampling of user trajectory data and parameter optimization based on a genetic algorithm. Our system generates an optimized quest play path, which is difficult to estimate. It is useful for preventing over rewarding in MMORPG quest design. In practice, the suggested systems can be applied easily to any type of MMORPG with scalability, and will be useful for reducing the manual cost for MMORPG development.

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References

  1. Ahsmore, C. & Nitsche, M. (2007). The quest in a generated World. In Proceedings of DiGRA.

  2. Blizzard, (2009). World of Warcraft. http://www.worldofwarcraft.com/.

  3. Borner, K., & Penumarthy, S. (2003). Social diffusion patterns in three-dimensional virtual worlds. Information Visualization, 2(3), 182–198.

    Article  Google Scholar 

  4. Byun, S. S., & Chuck, Y. (2008). Improving Inter-destination synchronization in hierarchical reliable multicast. Telecommunication Systems, 37(4), 203–216.

    Article  Google Scholar 

  5. Chittaro, L., Ranon, R., & Leronutti, L. (2006). VU-Flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12(6), 1475–1485.

    Article  Google Scholar 

  6. Doran, J. & Parberry, I. (2010). Towards procedural quest generation: A structural analysis of RPG quests. Technical Report LARC-2010-02.

  7. Goldberg, D.E., Lings, R., & Loci, A. (1985). Travel salesman problems. In Proceeding of an international conference on GA and their applications (pp. 154–162).

  8. Hart, P. E., Nilsson, N. J., & Raphael, B. (1972). A formal basis for the heuristic determination of minimum cost paths. SIGART Newsletter, 37, 28–29.

    Google Scholar 

  9. Hoobler, N., Humphreys, G., & Agrawala, M. (2004). Visualizing competitive behaviors in multi-user virtual environments. In Proceedings of the 15th IEEE visualization conference (pp. 163–170).

  10. Howard, J. (2006). Designing interpretative quests in the literature classroom. In Sandbox ‘06: Proceedings of the 2006 ACM SIGGRAPH symposium on videogames (pp. 133–138).

  11. Kang, Shin Jin. (2011). Procedural game level generation with interactive evolutionary computation. Journal of Future Game Technology, 1(1), 9–20.

    Google Scholar 

  12. Lee, K. G., & Han, S. K. (1994). Genetic algorithm for TSP by using union operator. The Journal of Korea Information Science, 21(1), 213–216.

    Google Scholar 

  13. Oliver, J.M., Smith, D.J., & Holland, J.R.C. (1987). A study of permutation crossover operators on the travel salesman problems. In Proceeding of the second ICGA (pp. 224–230).

  14. Peter, D., Panlop, Z., & Zahir, T. (2007). A dynamic priority approach to reducing delay in interactive TCP connections. Telecommunication Systems, 34(1), 59–70.

    Google Scholar 

  15. Rawlins, G. J. E. (Ed.). (1991). Foundation of genetic algorithms. San Francisco: Morgan Kaufmann.

    Google Scholar 

  16. Sas, C., O’Hare, G., & Reilly, R. (2004). Virtual environment trajectory analysis: A basis for navigational assistance and scene adaptivity. Future Generation Computer System, 21, 1157–1166.

    Article  Google Scholar 

  17. Sullivan, A. (2009). Gender-inclusive quest design in massively multiplayer online role-playing games. In Proceedings of the 4th international conference on foundations of digital games (pp. 354–356).

  18. Thawonmas, R., Hirano, M., & Kurashige, M. (2006). Cellular automata and hilditch thinning for extraction of user paths in online games. In Proceedings of 5th ACM SIGCOMM workshop on network and system support for games.

  19. Thawonmas, R., Kurashige, M., & Chen, K. T. (2007). Detection of landmarks for clustering of online-game players. The International Journal of Virtual Reality, 6(3), 11–16.

    Google Scholar 

  20. Tychsen, A., Tosca, S., & Brolund, T. (2006). Personalizing the player experience in MMORPGs. Lecture Notes in Computer Science, 4326 (pp. 253–264).

  21. Whitley, T. S., & Fuqua, D. (1989). Scheduling problems and the traveling salesman: The Genetic edge recombination operator. In Proceeding of the third ICGA (pp. 133–141).

  22. Yoshihiro, K., Tomonori, A., & Hiroyuki, M. (2004). A peer-to-peer message exchange scheme for large-scale networked virtual environments. Telecommunication Systems, 25(3), 353–370.

    Google Scholar 

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Acknowledgments

This work was supported by 2012 Hongik University Research Fund.

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Authors and Affiliations

  1. School of Games, Hongik University, Sejong, South Korea

    Shin Jin Kang

  2. Department of Game Engineering, Paichai University, Daejeon, South Korea

    Soo-Kyun Kim

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  1. Shin Jin Kang
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Correspondence to Soo-Kyun Kim.

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Kang, S.J., Kim, SK. Quest reward optimization method for massive multiplayer online role playing games. Telecommun Syst 60, 327–335 (2015). https://doi.org/10.1007/s11235-015-0033-6

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