2.50
Hdl Handle:
http://hdl.handle.net/10547/223778
Title:
Strategic team AI path plans: probabilistic pathfinding
Authors:
John, Tng C. H.; Prakash, Edmond C.; Chaudhari, Narendra S.
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.
Citation:
John, T., Prakash, E. & Chaudhari, N. (2008) 'Strategic team AI path plans: Probabilistic pathfinding', International Journal of Computer Games Technology, 2008, pp.1-6.
Publisher:
Hindawi
Journal:
International Journal of Computer Games Technology
Issue Date:
2008
URI:
http://hdl.handle.net/10547/223778
DOI:
10.1155/2008/834616
Additional Links:
http://www.hindawi.com/journals/ijcgt/2008/834616/
Type:
Article
Language:
en
ISSN:
1687-7047
Appears in Collections:
Centre for Computer Graphics and Visualisation (CCGV)

Full metadata record

DC FieldValue Language
dc.contributor.authorJohn, Tng C. H.en_GB
dc.contributor.authorPrakash, Edmond C.en_GB
dc.contributor.authorChaudhari, Narendra S.en_GB
dc.date.accessioned2012-05-15T10:55:52Z-
dc.date.available2012-05-15T10:55:52Z-
dc.date.issued2008-
dc.identifier.citationJohn, T., Prakash, E. & Chaudhari, N. (2008) 'Strategic team AI path plans: Probabilistic pathfinding', International Journal of Computer Games Technology, 2008, pp.1-6.en_GB
dc.identifier.issn1687-7047-
dc.identifier.doi10.1155/2008/834616-
dc.identifier.urihttp://hdl.handle.net/10547/223778-
dc.description.abstractThis 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.en_GB
dc.language.isoenen
dc.publisherHindawien_GB
dc.relation.urlhttp://www.hindawi.com/journals/ijcgt/2008/834616/en_GB
dc.rightsArchived with thanks to International Journal of Computer Games Technologyen_GB
dc.titleStrategic team AI path plans: probabilistic pathfindingen
dc.typeArticleen
dc.identifier.journalInternational Journal of Computer Games Technologyen_GB
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