Strategic Team AI Path Plans : Probabilistic Pathfinding

Abstract EN

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.