On Automated Discovery of Models Using Genetic Programming: Bargaining in a Three-Agent Coalitions Game
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Dworman:1995:JMIS,
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author = "Garett Dworman and Steven O. Kimbrough and
James D. Laing",
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title = "On Automated Discovery of Models Using Genetic
Programming: Bargaining in a Three-Agent Coalitions
Game",
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journal = "Journal of Management Information Systems",
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year = "1995",
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volume = "12",
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number = "3",
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pages = "97--125",
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month = "Winter",
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note = "Special Issue: Information Technology and IT
Organizational Impact Guest Editors: Nunamaker Jr, Jay
F and Sprague Jr., Ralph H",
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keywords = "genetic algorithms, genetic programming, automatic
model discovery, game theory, machine learning",
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ISSN = "0742-1222",
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URL = "http://www.jmis-web.org/articles/307",
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URL = "http://www.tandfonline.com/doi/abs/10.1080/07421222.1995.11518093",
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URL = "https://oid.wharton.upenn.edu/files/?whdmsaction=public:main.file&fileID=5434.",
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DOI = "doi:10.1080/07421222.1995.11518093",
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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.",
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notes = "fileID=5434. appears to be from Jstor.org",
- }
Genetic Programming entries for
Garett O Dworman
Steven Orla Kimbrough
James D Laing
Citations