Browse > Article
http://dx.doi.org/10.7583/JKGS.2015.15.4.157

Difficulty Evaluation of Game Levels using A Path-Finding Algorithm  

Chun, Youngjae (Dept. of Media, Soongsil university)
Oh, Kyoungsu (Dept. of Media, Soongsil university)
Publication Information
Journal of Korea Game Society / v.15, no.4, 2015 , pp. 157-168 More about this Journal
Abstract
The difficulty of the game is closely related to the fun of the game. However, it is not easy to determine the appropriate level of difficulty of the game. In most cases, human playtesting is required. But even so, it is still hard to quantitatively evaluate difficulty of the game. Thus, if we perform quantitative evaluation of the difficulty automatically it will be very helpful in game developments. In this paper, we use a path finding algorithm to evaluate difficulty of exploration in a game level. Exploration is a basic attribute in common video games and it represents the overall difficulty of the game level. We also optimize the proposed evaluation algorithm by using previous exploration histories when available area in an game level is dynamically expanded and the new search is required.
Keywords
Difficulty evaluation; Path finding algorithm; Game level;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Sorenson, N. and Pasquier, P., "Towards a generic framework for automated video game level creation", In Applications of Evolutionary Computation, pp. 131-140, 2010.
2 Sorenson, N. and Pasquier, P., "The evolution of fun: Automatic level design through challenge modeling", In Proceedings of the First International Conference on Computational Creativity, pp. 258-267, 2010.
3 Sorenson, N., Pasquier, P. and DiPaola, S., "A generic approach to challenge modeling for the procedural creation of video game levels", IEEE Transactions on Computational Intelligence and AI in Games, v.3, no.3, pp. 229-244. 2011.   DOI
4 Browne, C. and Maire, F., "Evolutionary game design", IEEE Transactions on Computational Intelligence and AI in Games, v.2, no.1, pp. 1-16. 2010.   DOI
5 Liapis, A., Yannakakis, G. N. and Togelius, J., "Towards a Generic Method of Evaluating Game Levels", In AIIDE, 2013.
6 Miyamoto, S., Yamauchi, H. and Tezuka, T., "Super Mario Bros", Nintendo, 1987.
7 Dechter, R. and Pearl, J., "Generalized best-first search strategies and the optimality of A*", Journal of the ACM (JACM), v.32, no.3, pp. 505-536, 1985.   DOI
8 Koenig, S. and Likhachev, M., "Improved fast replanning for robot navigation in unknown terrain", In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'02), v. 1, pp. 968-975, 2002.
9 Koenig, S. and Likhachev, M., "Incremental A*", In NIPS, pp. 1539-1546, 2001.
10 Likhachev, M., Ferguson, D. I., Gordon, G. J., Stentz, A. and Thrun, S., "Anytime Dynamic A*: An Anytime, Replanning Algorithm", In ICAPS, pp. 262-271, 2005.
11 Likhachev, M., Gordon, G. J. and Thrun, S., "ARA*: Anytime A* with provable bounds on sub-optimality", In Advances in Neural Information Processing Systems, 2003.
12 Bjornsson, Y. and Halldorsson, K., "Improved Heuristics for Optimal Path-finding on Game Maps", In AIIDE, pp. 9-14, 2006.
13 Oh-Ik Kwon and Teag-Keun Whangbo, "A Dynamic Path-Finding Method Avoiding Moving Obstacles in 3D Game Environment", Journal of Korea Game Society, v.6, no.3, pp.3-12, 2006.
14 John Olsen, "Attraction and Repulsors", Microsoft, Game Programming Gems, Charles River Media, 2004.
15 Sung Hyun Cho, "A Pathfinding Algorithm Using Path Information", Journal of Korea Game Society, v.13, no.1, pp.31-40, 2013.   DOI
16 Togelius, J., Karakovskiy, S. and Baumgarten, R., "The 2009 mario ai competition", In Evolutionary Computation (CEC), pp. 1-8, 2010.