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International Journal of Computer Games Technology
Volume 2008, Article ID 873913, 11 pages
Research Article

Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design

School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore 639798

Received 10 October 2007; Accepted 26 February 2008

Academic Editor: Kok Wai Wong

Copyright © 2008 Le Minh Duc et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Strategy game and simulation application are an exciting area with many opportunities for study and research. Currently most of the existing games and simulations apply hard coded rules so the intelligence of the computer generated forces is limited. After some time, player gets used to the simulation making it less attractive and challenging. It is also costly and tedious to incorporate new rules for an existing game. The main motivation behind this research project is to improve the quality of artificial intelligence- (AI-) based on various techniques such as qualitative spatial reasoning (Forbus et al., 2002), near-optimal hierarchical pathfinding (HPA*) (Botea et al., 2004), and reinforcement learning (RL) (Sutton and Barto, 1998).