基于计划知识图的未来行动预测研究

Zengyu Cai, Yuan Feng, Jianwei Zhang, Baowei Zhang
{"title":"基于计划知识图的未来行动预测研究","authors":"Zengyu Cai, Yuan Feng, Jianwei Zhang, Baowei Zhang","doi":"10.1109/ISRA.2012.6219354","DOIUrl":null,"url":null,"abstract":"Plan recognition is an important field in artificial intelligence. In this paper, a new plan recognition algorithm based on plan knowledge graph to predict future actions was presented. The algorithm is more powerful and simpler, comparing with other algorithms. It can be used to handle the condition of partial observation and predict future actions. The experimental results show that the algorithm is linear-time with the domain knowledge, and it powerful than Jiang's algorithms. The studies on event relations in plan recognition are not benefit to improve the algorithms in plan knowledge graph framework, but also are helpful for improving other recognition algorithms and developing new algorithms.","PeriodicalId":266930,"journal":{"name":"2012 IEEE Symposium on Robotics and Applications (ISRA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research on predicting future actions based on plan knowledge graph\",\"authors\":\"Zengyu Cai, Yuan Feng, Jianwei Zhang, Baowei Zhang\",\"doi\":\"10.1109/ISRA.2012.6219354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plan recognition is an important field in artificial intelligence. In this paper, a new plan recognition algorithm based on plan knowledge graph to predict future actions was presented. The algorithm is more powerful and simpler, comparing with other algorithms. It can be used to handle the condition of partial observation and predict future actions. The experimental results show that the algorithm is linear-time with the domain knowledge, and it powerful than Jiang's algorithms. The studies on event relations in plan recognition are not benefit to improve the algorithms in plan knowledge graph framework, but also are helpful for improving other recognition algorithms and developing new algorithms.\",\"PeriodicalId\":266930,\"journal\":{\"name\":\"2012 IEEE Symposium on Robotics and Applications (ISRA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Symposium on Robotics and Applications (ISRA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRA.2012.6219354\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Symposium on Robotics and Applications (ISRA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRA.2012.6219354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

计划识别是人工智能的一个重要研究领域。本文提出了一种新的基于计划知识图的计划识别算法来预测未来行动。与其他算法相比,该算法功能更强大、更简单。它可以用来处理部分观测的情况和预测未来的行动。实验结果表明,该算法在具有领域知识的情况下是线性时间的,比Jiang的算法更强大。对计划识别中事件关系的研究不仅有利于改进计划知识图框架中的算法,而且对改进其他识别算法和开发新算法也有帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on predicting future actions based on plan knowledge graph
Plan recognition is an important field in artificial intelligence. In this paper, a new plan recognition algorithm based on plan knowledge graph to predict future actions was presented. The algorithm is more powerful and simpler, comparing with other algorithms. It can be used to handle the condition of partial observation and predict future actions. The experimental results show that the algorithm is linear-time with the domain knowledge, and it powerful than Jiang's algorithms. The studies on event relations in plan recognition are not benefit to improve the algorithms in plan knowledge graph framework, but also are helpful for improving other recognition algorithms and developing new algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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