Strategic Team AI Path Plans: Probabilistic Pathfinding
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2008-01-01
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| Series: | International Journal of Computer Games Technology |
| Online Access: | http://dx.doi.org/10.1155/2008/834616 |
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| Summary: | 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. |
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| ISSN: | 1687-7047 1687-7055 |