Strategic Team AI Path Plans: Probabilistic Pathfinding. (16th December 2007)

• Record Type: Journal Article
• Authors: John, Tng C. H.; Prakash, Edmond C.; Chaudhari, Narendra S.
• Other Names: Wong Kok Wai Academic Editor.
• Is Part Of: International journal of computer games technology. Volume 2008(2008)
• Publication Date: 2007-12-16
• Subjects: Computer games -- Design -- Periodicals; Computer games -- Programming -- Periodicals; Jeux d'ordinateur; Jeux d'ordinateur -- Programmation; Computer games -- Design; Computer games -- Programming; Electronic journals; Periodicals; 794.81
• DOI: 10.1155/2008/834616
• Languages: English
• ISSNs: 1687-7047

Abstract

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.