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Wonders of Seabed: Difficulty Evaluation of Management Games Using Neural Network

  • Cheng-Yi Huang
  • Yi-Cheng Lee
  • Chia-An Yu
  • Yi-Zheng Lee
  • Sai-Keung Wong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8916)

Abstract

In management games, players enjoy developing the virtual objects, such as avatars, farms, villages, cities, resources, etc. Usually, the management games do not have a time limit to play. Players know little about how much time that they need to devote in order to quickly build up the virtual objects. This paper studies about the difficulty level of the management games. That is, under a limit amount of time, how far a player can develop the objects. We propose to adopt a neural network to evaluate the difficulty of such kind of games. There is a diverse set of features supported by management games. Thus, we have developed a manageable management game called Wonders of Seabed. Our game is a 3D game and the game story happens at the seabed. Players need to develop a city by managing different resources. Our method can adjust the game difficulty for the players.

Keywords

management games game balance game evaluation neural network game difficulty 

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References

  1. 1.
    Real-time game adaptation for optimizing player satisfaction. IEEE Transactions on Computational Intelligence and AI in Games 1(2), 121–133 (2009)Google Scholar
  2. 2.
    Game object model version ii: a theoretical framework for educational game development. Educational Technology Research and Development 55(1) (2007)Google Scholar
  3. 3.
    Andrade, G., Ramalho, G., Gomes, S., Corruble, V.: Dynamic game balancing: An evaluation of user satisfaction. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 3–8 (2006)Google Scholar
  4. 4.
    Andrade, G., Ramalho, G., Santana, H., Corruble, V.: Challenge-sensitive action selection: an application to game balancing. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 194–200 (2005)Google Scholar
  5. 5.
    Andrade, G., Ramalho, G., Santana, H., Corruble, V.: Extending reinforcement learning to provide dynamic game balancing. In: Proceedings of the Workshop on Reasoning, Representation, and Learning in Computer Games, 19th International Joint Conference on Artificial Intelligence (IJCAI), pp. 7–12 (2005)Google Scholar
  6. 6.
    Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., Berta, R.: Assessment in and of serious games: An overview. Adv. in Hum.-Comp. Int. 2013, 1:1–1:11 (2013)Google Scholar
  7. 7.
    de Ribaupierre, S., Kapralos, B., Haji, F., Stroulia, E., Dubrowski, A., Eagleson, R.: Healthcare training enhancement through virtual reality and serious games 68, 9–27 (2014)Google Scholar
  8. 8.
    Fang, S.-W., Wong, S.-K.: Game team balancing by using particle swarm optimization. Knowledge-Based Systems 34, 91–96 (2012)CrossRefGoogle Scholar
  9. 9.
    Hu, X., Eberhart, R.C., Shi, Y.: Swarm intelligence for permutation optimization: a case study of n-queens problem. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 243–246 (2003)Google Scholar
  10. 10.
    Hunicke, R., Chapman, V.: Ai for dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop, pp. 91–97 (2004)Google Scholar
  11. 11.
    Huth, A., Wissel, C.: The simulation of the movement of fish schools. Journal of Theoretical Biology 156(3), 365–385 (1992)CrossRefGoogle Scholar
  12. 12.
    Iwasaki, K., Dobashi, Y., Nishita, T.: Efficient rendering of optical effects within water using graphics hardware. In: Proceedings of Ninth Pacific Conference on Computer Graphics and Applications, pp. 374–383 (2001)Google Scholar
  13. 13.
    Lee, C.-C.: A self-learning rule-based controller employing approximate reasoning and neural net concepts. International Journal of Intelligent Systems 6(1), 71–93 (2007)CrossRefGoogle Scholar
  14. 14.
    Messerschmidt, L., Engelbrecht, A.: Learning to play games using a pso-based competitive learning approach. IEEE Transactions on Evolutionary Computation 8(3), 280–288 (2004)CrossRefGoogle Scholar
  15. 15.
    Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Difficulty scaling of game ai. In: Proc. Fifth Int’l Conf. Intelligent Games and Simulation, pp. 33–37 (2004)Google Scholar
  16. 16.
    Stanley, K.O.: Evolving neural network agents in the nero video game. In: Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, pp. 182–189 (2005)Google Scholar
  17. 17.
    Tan, C.-H., Tan, K.-C., Tay, A.: Dynamic game difficulty scaling using adaptive behavior-based ai. IEEE Transactions on Computational Intelligence and AI in Games 3(4), 289–301 (2011)CrossRefGoogle Scholar
  18. 18.
    Thrun, S.: Learning to play the game of chess. Advances in Neural Information Processing Systems 7, 1069–1076 (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng-Yi Huang
    • 1
  • Yi-Cheng Lee
    • 1
  • Chia-An Yu
    • 1
  • Yi-Zheng Lee
    • 1
  • Sai-Keung Wong
    • 1
  1. 1.National Chiao Tung UniversityTaiwan (R.O.C.)

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