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http://dx.doi.org/10.7583/JKGS.2015.15.5.89

Making Levels More Challenging with a Cooperative Strategy of Ghosts in Pac-Man  

Choi, Taeyeong (School of Computer Science and Engineering, Soongsil University)
Na, Hyeon-Suk (School of Computer Science and Engineering, Soongsil University)
Publication Information
Journal of Korea Game Society / v.15, no.5, 2015 , pp. 89-98 More about this Journal
Abstract
The artificial intelligence (AI) of Non-Player Companions (NPC), especially opponents, is a key element to adjust the level of games in game design. Smart opponents can make games more challenging as well as allow players for diverse experiences, even in the same game environment. Since game users interact with more than one opponent in most of today's games, collaboration control of opponent characters becomes more important than ever before. In this paper, we introduce a cooperative strategy based on the A* algorithm for enemies' AI in the Pac-Man game. A survey from 17 human testers shows that the levels with our collaborative opponents are more difficult but interesting than those with either the original Pac-Man's personalities or the non-cooperative greedy opponents.
Keywords
Artificial Intelligence; Game Level Design; Behavior Pattern of NPC; Cooperative Multi-agents;
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  • Reference
1 P. Stone and M. Veloso, "Multiagent systems: A survey from a machine learning perspective", Autonomous Robots, Vol. 8, No. 3, pp. 345-383, 2000.   DOI
2 M. Benda, V. Jagannathan, and R. Dodhiawalla, "On optimal cooperation of knowledge sources", Technical Report No.BCS-G2010-28, Boeing Advanced Technology Center, 1986.
3 L.M. Sephens and M.B. Merx, "The effect of agent control strategy on the performance of a DAI pursuit problem", In Proceeding of the the 10th International Workshop on Distributed Artificial Intelligence, 1990.
4 T. Haynes and S. Sen, "Evolving behaviora1 strategies in predators and prey", Adaptation and Leaming in Multiagent System, Springer Verlag, Berlin, pp.113-126, 1996.
5 Y. Ishiwaka, T. Sato, and Y. Kakazu, "An approach to the pursuit problem on a heterogeneous multiagent system using reinforcement learning", Elsevier Journal on Robotics and Autonomous Systems, Vol. 43, No. 4, pp. 245-256, 2003.   DOI
6 Hyong-Ill Lee and Byung-Cheon Kim, "Multiagent Control Strategy Using Reinforcement Learning", The KIPS Transactions: PartB, Vol. 10B, No. 3, pp. 249-256, 2003.   DOI
7 D. Xiao and A. Tan, "Cooperative cognitive agents and reinforcement learning in pursuit game", In Proceedings of 3rd Int'l Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS'05), 2005.
8 C. Undeger and F. Polat, "Multi-agent real-time pursuit", Autonomous Agents and Multi-Agent Systems, Vol. 21, No. 1, pp. 69-107, 2010.   DOI
9 K. Q. Nguyen and R. Thawonmas, "Monte carlo tree search for collaboration control of ghosts in Ms. Pac-Man", IEEE Transactions on Computational Intelligence and AI in Games, Vol 5, No. 1, pp. 57-68, 2013.   DOI