Rule-Based System Against Reinforcement Learning*

Bozhan I. Orozov, D. Orozova
{"title":"Rule-Based System Against Reinforcement Learning*","authors":"Bozhan I. Orozov, D. Orozova","doi":"10.1145/3472410.3472437","DOIUrl":null,"url":null,"abstract":"Reinforcement Learning is becoming an increasingly popular type of machine learning. In it, artificial intelligence learns what the best action in each given situation is and over time optimizes the decisions it makes. On the other hand, provided that sufficiently good rules are created, a Rule-Based System is a program task that can support very complex behavior. The aim of the authors in this study is to analyze and compare the behavior of two agents, realized on the basis of these two approaches.","PeriodicalId":115575,"journal":{"name":"Proceedings of the 22nd International Conference on Computer Systems and Technologies","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd International Conference on Computer Systems and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3472410.3472437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Reinforcement Learning is becoming an increasingly popular type of machine learning. In it, artificial intelligence learns what the best action in each given situation is and over time optimizes the decisions it makes. On the other hand, provided that sufficiently good rules are created, a Rule-Based System is a program task that can support very complex behavior. The aim of the authors in this study is to analyze and compare the behavior of two agents, realized on the basis of these two approaches.
基于规则的系统对抗强化学习*
强化学习正在成为一种越来越受欢迎的机器学习类型。在其中,人工智能学习在每种给定情况下的最佳行动,并随着时间的推移优化其做出的决策。另一方面,只要创建了足够好的规则,基于规则的系统就是一个可以支持非常复杂行为的程序任务。作者在本研究中的目的是分析和比较基于这两种方法实现的两个代理的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信