Published Articles >> Table of Contents >> Abstract

28th Hawaii International Conference on System Sciences (HICSS'95)   p. 428
On automated discovery of models using genetic programming in game-theoretic contexts
G. Dworman, Wharton Sch., Pennsylvania Univ., Philadelphia, PA, USA
S.O. Kimbrough, Wharton Sch., Pennsylvania Univ., Philadelphia, PA, USA
J.D. Laing, Wharton Sch., Pennsylvania Univ., Philadelphia, PA, USA

Full Article Text:

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HICSS.1995.375625
Send link to a friend

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.
Back to Top Additional Information
Index Terms- learning (artificial intelligence); genetic algorithms; game theory; genetic programming; game-theory; mathematical models; qualitative models; evolutionary computation; machine learning techniques; high-quality negotiation policies; three-player coalitions game Citation:  G. Dworman, S.O. Kimbrough, J.D. Laing, "On automated discovery of models using genetic programming in game-theoretic contexts," hicss, p. 428,  28th Hawaii International Conference on System Sciences (HICSS'95),  1995