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.
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References
Real-time game adaptation for optimizing player satisfaction. IEEE Transactions on Computational Intelligence and AI in Games 1(2), 121–133 (2009)
Game object model version ii: a theoretical framework for educational game development. Educational Technology Research and Development 55(1) (2007)
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)
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)
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)
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)
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)
Fang, S.-W., Wong, S.-K.: Game team balancing by using particle swarm optimization. Knowledge-Based Systems 34, 91–96 (2012)
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)
Hunicke, R., Chapman, V.: Ai for dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop, pp. 91–97 (2004)
Huth, A., Wissel, C.: The simulation of the movement of fish schools. Journal of Theoretical Biology 156(3), 365–385 (1992)
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)
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)
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)
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)
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)
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)
Thrun, S.: Learning to play the game of chess. Advances in Neural Information Processing Systems 7, 1069–1076 (1995)
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Huang, CY., Lee, YC., Yu, CA., Lee, YZ., Wong, SK. (2014). Wonders of Seabed: Difficulty Evaluation of Management Games Using Neural Network. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_16
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DOI: https://doi.org/10.1007/978-3-319-13987-6_16
Publisher Name: Springer, Cham
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