e-space
Manchester Metropolitan University's Research Repository

Strategic team AI path plans: probabilistic pathfinding

John, Tng C. H. and Prakash, Edmond C. and Chaudhari, Narendra S. (2008) Strategic team AI path plans: probabilistic pathfinding. ISSN 1687-7055

Full text not available from this repository.

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.

Impact and Reach

Statistics

Downloads
Activity Overview
0Downloads
46Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

Edit Item Edit Item