Evolving Third-Person Shooter Enemies to Optimize Player Satisfaction in Real-Time

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Font:evoapps12,

• author = "Jose M. Font",
• title = "Evolving Third-Person Shooter Enemies to Optimize Player Satisfaction in Real-Time",
• booktitle = "Applications of Evolutionary Computing, EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC",
• year = "2011",
• month = "11-13 " # apr,
• editor = "Cecilia {Di Chio} and Alexandros Agapitos and Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and Gianni A. {Di Caro} and Rolf Drechsler and Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and William B. Langdon and Juan J. Merelo and Mike Preuss and Hendrik Richter and Sara Silva and Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and Julian Togelius and Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis",
• series = "LNCS",
• volume = "7248",
• publisher = "Springer Verlag",
• address = "Malaga, Spain",
• publisher_address = "Berlin",
• pages = "204--213",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, Evolutionary computation, fuzzy rule based system, grammar-guided genetic programming, player satisfaction",
• isbn13 = "978-3-642-29177-7",
• DOI = "doi:10.1007/978-3-642-29178-4_21",
• abstract = "A grammar-guided genetic program is presented to automatically build and evolve populations of AI controlled enemies in a 2D third-person shooter called Genes of War. This evolutionary system constantly adapts enemy behaviour, encoded by a multi-layered fuzzy control system, while the game is being played. Thus the enemy behaviour fits a target challenge level for the purpose of maximising player satisfaction. Two different methods to calculate this challenge level are presented: 'hardwired' that allows the desired difficulty level to be programed at every stage of the gameplay, and 'adaptive' that automatically determines difficulty by analysing several features extracted from the player's gameplay. Results show that the genetic program successfully adapts armies of ten enemies to different kinds of players and difficulty distributions.",
• notes = "EvoGames Part of \cite{DiChio:2012:EvoApps} EvoApplications2012 held in conjunction with EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",

}

Genetic Programming entries for Jose M Font

Citations