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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| _version_ | 1832545702370082816 |
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| author | Tng C. H. John Edmond C. Prakash Narendra S. Chaudhari |
| author_facet | Tng C. H. John Edmond C. Prakash Narendra S. Chaudhari |
| author_sort | Tng C. H. John |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d0be3f56aec44be85606116d1b5e976 |
| institution | Kabale University |
| issn | 1687-7047 1687-7055 |
| language | English |
| publishDate | 2008-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Computer Games Technology |
| spelling | doaj-art-2d0be3f56aec44be85606116d1b5e9762025-02-03T07:24:51ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552008-01-01200810.1155/2008/834616834616Strategic Team AI Path Plans: Probabilistic PathfindingTng C. H. John0Edmond C. Prakash1Narendra S. Chaudhari2School of Computer Engineering, Nanyang Technological University, 639798, SingaporeDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UKSchool of Computer Engineering, Nanyang Technological University, 639798, SingaporeThis 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.http://dx.doi.org/10.1155/2008/834616 |
| spellingShingle | Tng C. H. John Edmond C. Prakash Narendra S. Chaudhari Strategic Team AI Path Plans: Probabilistic Pathfinding International Journal of Computer Games Technology |
| title | Strategic Team AI Path Plans: Probabilistic Pathfinding |
| title_full | Strategic Team AI Path Plans: Probabilistic Pathfinding |
| title_fullStr | Strategic Team AI Path Plans: Probabilistic Pathfinding |
| title_full_unstemmed | Strategic Team AI Path Plans: Probabilistic Pathfinding |
| title_short | Strategic Team AI Path Plans: Probabilistic Pathfinding |
| title_sort | strategic team ai path plans probabilistic pathfinding |
| url | http://dx.doi.org/10.1155/2008/834616 |
| work_keys_str_mv | AT tngchjohn strategicteamaipathplansprobabilisticpathfinding AT edmondcprakash strategicteamaipathplansprobabilisticpathfinding AT narendraschaudhari strategicteamaipathplansprobabilisticpathfinding |