Towards Automatic StarCraft Strategy Generation Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Pablo Garcia-Sanchez and Alberto Tonda and 
                 Antonio Mora and Giovanni Squillero and J. J. Merelo",
  title =        "Towards Automatic StarCraft Strategy Generation Using
                 Genetic Programming",
  booktitle =    "Proceedings of the IEEE Conference on Computational
                 Intelligence and Games (CIG-2015)",
  year =         "2015",
  editor =       "Shi-Jim Yen and Tristan Cazenave and Philip Hingston",
  pages =        "284--291",
  address =      "Tainan, Taiwan",
  month =        aug # " 31-" # sep # " 2",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, microGP,
  URL =          "",
  DOI =          "doi:10.1109/CIG.2015.7317940",
  size =         "8 pages",
  abstract =     "Among Real-Time Strategy games few titles have enjoyed
                 the continued success of StarCraft. Many research lines
                 aimed at developing Artificial Intelligences, or bots,
                 capable of challenging human players, use StarCraft as
                 a platform. Several characteristics make this game
                 particularly appealing for researchers, such as:
                 asymmetric balanced factions, considerable complexity
                 of the technology trees, large number of units with
                 unique features, and potential for optimization both at
                 the strategical and tactical level. In literature,
                 various works exploit evolutionary computation to
                 optimize particular aspects of the game, from squad
                 formation to map exploration; but so far, no
                 evolutionary approach has been applied to the
                 development of a complete strategy from scratch. In
                 this paper, we present the preliminary results of
                 StarCraftGP, a framework able to evolve a complete
                 strategy for StarCraft, from the building plan, to the
                 composition of squads, up to the set of rules that
                 define the bot's behaviour during the game. The
                 proposed approach generates strategies as C++ classes,
                 that are then compiled and executed inside the
                 OpprimoBot open-source framework. In a first set of
                 runs, we demonstrate that StarCraftGP ultimately
                 generates a competitive strategy for a Zerg bot, able
                 to defeat several human-designed bots.",
  notes =        "Turing complete, obtain competitive bots from scratch,
                 compiled C++ classes (constructor and per frame
                 method). 2014 AIIDE competition OpprimoBot Benzene.scx
                 evolves only high level strategies. p285 'being
                 non-human is, in fact, one of the main advantages of
                 GP'. p288 'evolve a Zerg strategy'. parallel 8
                 VirtualBox. DLL p290 'RTS games' p290 'automatically
                 generate strategies that can defeat bots hand-coded by
                 human experts'. Cites \cite{Sanchez:uGP:book}.


                 Entered 2016 HUMIES",

Genetic Programming entries for Pablo Garcia-Sanchez Alberto Tonda Antonio M Mora Garcia Giovanni Squillero Juan Julian Merelo