Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms
复制标题
基于遗传算法的规则发现系统中的信用分配
DOI：
10.1023/a:1022614421909
复制
发表时间：
2005
期刊：
影响因子：
7.5
通讯作者：
J. Grefenstette
中科院分区：
文献类型：
--
作者：
J. Grefenstette
在规则发现系统中，学习通常首先评估系统当前规则的质量，然后根据评估修改规则。本文研究了当长序列的规则在连续的外部奖励之间触发时出现的信用分配问题。重点讨论了使用遗传算法作为主要规则修改策略的规则发现系统所提出的各种规则评估方案。以前已经报道了两种不同的遗传算法规则学习方法，每种方法都为不同级别的信用分配问题提供了有用的解决方案。我们描述了一个称为RUDI的系统，它利用了这两种方法。我们提出了分析和实验结果，支持多级信用分配可以提高基于遗传算法的规则学习系统性能的假设。
In rule discovery systems, learning often proceeds by first assessing the quality of the system's current rules and then modifying rules based on that assessment. This paper addresses the credit assignment problem that arises when long sequences of rules fire between successive external rewards. The focus is on the kinds of rule assessment schemes which have been proposed for rule discovery systems that use genetic algorithms as the primary rule modification strategy. Two distinct approaches to rule learning with genetic algorithms have been previously reported, each approach offering a useful solution to a different level of the credit assignment problem. We describe a system, called RUDI, that exploits both approaches. We present analytic and experimental results that support the hypothesis that multiple levels of credit assignment can improve the performance of rule learning systems based on genetic algorithms.