Evolving Game State Features from Raw Pixels

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Baozhu Jia and Marc Ebner",
  title =        "Evolving Game State Features from Raw Pixels",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "52--63",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-55696-3_4",
  abstract =     "General video game playing is the art of designing
                 artificial intelligence programs that are capable of
                 playing different video games with little domain
                 knowledge. One of the great challenges is how to
                 capture game state features from different video games
                 in a general way. The main contribution of this paper
                 is to apply genetic programming to evolve game state
                 features from raw pixels. A voting method is
                 implemented to determine the actions of the game agent.
                 Three different video games are used to evaluate the
                 effectiveness of the algorithm: Missile Command,
                 Frogger, and Space Invaders. The results show that
                 genetic programming is able to find useful game state
                 features for all three games.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Baozhu Jia Marc Ebner