Learning to compose fuzzy behaviors for autonomous agents

被引：15

作者：

Bonarini, A

Basso, F

机构：

[1] Politec. Milano AI Robotics Proj., Dipto. di Elettronica e Informazione, Politecnico di Milano, Milano

[2] Dipto. di Elettronica e Informazione, Politecnico di Milano, 20133 Milano, Piazza Leonardo da Vinci

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 1997年 / 17卷 / 04期

关键词：

reinforcement learning; fuzzy control; autonomous agents;

D O I：

10.1016/S0888-613X(97)00002-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fully logic controllers implementing fuzzy behaviors for an autonomous agent. S-ELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how S-ELF can learn robust and portable behaviors, thus reducing the time and effort to design behavior-based agents. (C) 1997 Elsevier Science Inc.

引用

页码：409 / 432

页数：24