期刊名称：International Journal of Computer Games Technology
印刷版ISSN：1687-7047
电子版ISSN：1687-7055
出版年度：2008
卷号：2008
DOI：10.1155/2008/834616
出版社：Hindawi Publishing Corporation
摘要：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.
