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International Journal of Computer Games Technology
Volume 2008 (2008), Article ID 834616, 6 pages
http://dx.doi.org/10.1155/2008/834616
Research Article

Strategic Team AI Path Plans: Probabilistic Pathfinding

1School of Computer Engineering, Nanyang Technological University, Singapore 639798
2Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK

Received 29 September 2007; Accepted 13 December 2007

Academic Editor: Kok Wai Wong

Copyright © 2008 Tng C. H. John 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.

Abstract

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.