Learning equilibrium as a generalization of learning to optimize

被引：1

作者：

Monderer, Dov ^{[1
]}

Tennenholtz, Moshe ^{[1
]}

机构：

[1] Technion Israel Inst Technol, Fac Ind Engn & Management, IL-32000 Haifa, Israel

来源：

ARTIFICIAL INTELLIGENCE | 2007年 / 171卷 / 07期

关键词：

learning; machine learning; learning equilibrium;

D O I：

10.1016/j.artint.2007.01.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We argue that learning equilibrium is an appropriate generalization to multi-agent systems of the concept of learning to optimize in single-agent setting. We further define and discuss the concept of weak learning equilibrium. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：448 / 452

页数：5

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