On Automated Discovery of Models Using Genetic
Programming in Game-Theoretic Contexts
G. Dworman, S. Kimbrough, и J. Laing. Proceedings of the 28th Hawaii International
Conference on System Sciences, Volume III: Information
Systems: Decision Support and Knowledge-based Systems, стр. 428--438. IEEE Computer Society Press, (января 1995)
Аннотация
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.
Proceedings of the 28th Hawaii International
Conference on System Sciences, Volume III: Information
Systems: Decision Support and Knowledge-based Systems
%0 Conference Paper
%1 dworman:1996:admGPgtc
%A Dworman, Garett
%A Kimbrough, Steve O.
%A Laing, James D.
%B Proceedings of the 28th Hawaii International
Conference on System Sciences, Volume III: Information
Systems: Decision Support and Knowledge-based Systems
%D 1995
%E Nunamaker Jr., Jay F.
%E Sprague Jr., Ralph H.
%I IEEE Computer Society Press
%K algorithms, genetic programming
%P 428--438
%T On Automated Discovery of Models Using Genetic
Programming in Game-Theoretic Contexts
%U http://doi.ieeecomputersociety.org/10.1109/HICSS.1995.375625
%X 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.
@inproceedings{dworman:1996:admGPgtc,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Dworman, Garett and Kimbrough, Steve O. and Laing, James D.},
biburl = {https://www.bibsonomy.org/bibtex/2f4252e30fcd562137758663123076628/brazovayeye},
booktitle = {Proceedings of the 28th Hawaii International
Conference on System Sciences, Volume III: Information
Systems: Decision Support and Knowledge-based Systems},
broken = {http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6.ps},
editor = {{Nunamaker Jr.}, Jay F. and {Sprague Jr.}, Ralph H.},
interhash = {d5a4fa3506d2b09a155227e65fda9d09},
intrahash = {f4252e30fcd562137758663123076628},
keywords = {algorithms, genetic programming},
month = {January},
pages = {428--438},
publisher = {IEEE Computer Society Press},
publisher_address = {Los Alamitos, CA},
size = {13 pages},
timestamp = {2008-06-19T17:39:00.000+0200},
title = {On Automated Discovery of Models Using Genetic
Programming in Game-Theoretic Contexts},
url = {http://doi.ieeecomputersociety.org/10.1109/HICSS.1995.375625},
year = 1995
}