Toward a model of intelligence as an economy of agents

被引：23

作者：

Baum, EB ^{[1
]}

机构：

[1] NEC Res Inst, Princeton, NJ 08540 USA

来源：

MACHINE LEARNING | 1999年 / 35卷 / 02期

关键词：

reinforcement learning; multi-agent systems; planning; evolutionary economics; tragedy of the commons; classifier systems; agoric systems; autonomous programming; cognition; artificial intelligence; Hayek; complex adaptive systems; temporal difference learning; evolutionary computation; economic models of mind; economic models of computation; Blocks World; reasoning; learning; computational learning theory; learning to reason; meta-reasoning;

D O I：

10.1023/A:1007593124513

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A market-based algorithm is presented which autonomously apportions complex tasks to multiple cooperating agents giving each agent the motivation of improving performance of the whole system. A specific model, called "The Hayek Machine" is proposed and tested on a simulated Blocks World (BW) planning problem. Hayek learns to solve more complex BW problems than any previous learning algorithm. Given intermediate reward and simple features, it has learned to efficiently solve arbitrary BW problems. The Hayek Machine can also be seen as a model of evolutionary economics.

引用

页码：155 / 185

页数：31