Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem 


Vol. 15,  No. 3, pp. 253-258, Jun.  2008
10.3745/KIPSTB.2008.15.3.253


PDF
  Abstract

Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules

  Statistics
  Cite this article

[IEEE Style]

O. K. Kwon and J. K. Park, "Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem," The KIPS Transactions:PartB , vol. 15, no. 3, pp. 253-258, 2008. DOI: 10.3745/KIPSTB.2008.15.3.253.

[ACM Style]

O Kyang Kwon and Jong Koo Park. 2008. Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem. The KIPS Transactions:PartB , 15, 3, (2008), 253-258. DOI: 10.3745/KIPSTB.2008.15.3.253.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals