On Automated Discovery of Models Using Genetic Programming: Bargaining in a Three-Agent Coalitions Game

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

@Article{Dworman:1995:JMIS,
  author =       "Garett Dworman and Steven O. Kimbrough and 
                 James D. Laing",
  title =        "On Automated Discovery of Models Using Genetic
                 Programming: Bargaining in a Three-Agent Coalitions
                 Game",
  journal =      "Journal of Management Information Systems",
  year =         "1995",
  volume =       "12",
  number =       "3",
  pages =        "97--125",
  month =        "Winter",
  note =         "Special Issue: Information Technology and IT
                 Organizational Impact Guest Editors: Nunamaker Jr, Jay
                 F and Sprague Jr., Ralph H",
  keywords =     "genetic algorithms, genetic programming, automatic
                 model discovery, game theory, machine learning",
  ISSN =         "0742-1222",
  URL =          "http://www.jmis-web.org/articles/307",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/07421222.1995.11518093",
  URL =          "https://oid.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=5434.",
  DOI =          "doi:10.1080/07421222.1995.11518093",
  abstract =     "The creation of mathematical, as well as qualitative
                 (or rule-based), models is difficult, time-consuming,
                 and expensive. Recent developments in evolutionary
                 computation hold out the prospect that, for many
                 problems of practical import, machine learning
                 techniques can be used to discover useful models
                 automatically. The prospects are particularly bright,
                 we believe, for such automated discoveries in the
                 context of game theory. This paper reports on a series
                 of successful experiments in which we used a genetic
                 programming regime to discover high-quality negotiation
                 policies. The game-theoretic context in which we
                 conducted these experiments--a three-player coalitions
                 game with side payments--is considerably more complex
                 and subtle than any reported in the previous literature
                 on machine learning applied to game theory.",
  notes =        "fileID=5434. appears to be from Jstor.org",
}

Genetic Programming entries for Garett O Dworman Steven Orla Kimbrough James D Laing

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