@inproceedings{dworman:1996:admGPgtc,
	title = {On Automated Discovery of Models Using Genetic
                 Programming in Game-Theoretic Contexts},
	author = {Garett Dworman and Steve O. Kimbrough and James D. Laing},
	booktitle = {Proceedings of the 28th Hawaii International
                 Conference on System Sciences, Volume III: Information
                 Systems: Decision Support and Knowledge-based Systems},
	editor = {Jay F. {Nunamaker Jr.} and Ralph H. {Sprague Jr.}},
	month = {January},
	pages = {428--438},
	publisher = {IEEE Computer Society Press},
	url = {http://doi.ieeecomputersociety.org/10.1109/HICSS.1995.375625},
	year = {1995},
	biburl = {http://www.bibsonomy.org/bibtex/2f4252e30fcd562137758663123076628/brazovayeye},
	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. These 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 sidepayments-is considerably more complex and
                 subtle than any reported in the literature on machine
                 learning applied to game theory.},
	publisher_address = {Los Alamitos, CA}, size = {13 pages}, broken = {http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6.ps},
	keywords = {algorithms, genetic programming }
}