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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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