Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1155/2008/834616 | Int. J. Computer Games Technology |
Keywords | Field | DocType |
path planning | Pathfinding,Simulation,Computer science,Fitness function,Artificial intelligence,Probabilistic logic,Genetic algorithm,Machine learning | Journal |
Volume | ISSN | Citations |
2008, | 1687-7047 | 3 |
PageRank | References | Authors |
0.48 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hou C. Tng John | 1 | 3 | 0.48 |
| Edmond C. Prakash | 2 | 102 | 14.60 |
| Narendra S. Chaudhari | 3 | 152 | 37.21 |