{"title":"基于多项式近似的学习搜索","authors":"Wei Zhang, Shenggui Hong","doi":"10.1109/TENCON.1993.320107","DOIUrl":null,"url":null,"abstract":"In this paper, polynomial approximation method and theory are introduced into the research of learning search of artificial intelligence. By so doing, a learning search algorithm can, after sufficient number of problem-solving, construct a heuristic estimate function h(.) which uniformly approximates to the optimal estimate function h*(.) by arbitrary precision. One of these learning search algorithms, A-B/sub n/, is described and it is shown that, when the number of the previous problem-solving becomes large enough, the worst-case complexity of A-B/sub n/ can be reduced to O(poly(N)), where N is the length of the optimal solution path, poly(N) is a polynomial function of N.<<ETX>>","PeriodicalId":110496,"journal":{"name":"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Polynomial approximation based learning search\",\"authors\":\"Wei Zhang, Shenggui Hong\",\"doi\":\"10.1109/TENCON.1993.320107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, polynomial approximation method and theory are introduced into the research of learning search of artificial intelligence. By so doing, a learning search algorithm can, after sufficient number of problem-solving, construct a heuristic estimate function h(.) which uniformly approximates to the optimal estimate function h*(.) by arbitrary precision. One of these learning search algorithms, A-B/sub n/, is described and it is shown that, when the number of the previous problem-solving becomes large enough, the worst-case complexity of A-B/sub n/ can be reduced to O(poly(N)), where N is the length of the optimal solution path, poly(N) is a polynomial function of N.<<ETX>>\",\"PeriodicalId\":110496,\"journal\":{\"name\":\"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON.1993.320107\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.1993.320107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
本文将多项式逼近方法和理论引入到人工智能学习搜索的研究中。这样,学习搜索算法在求解足够多的问题后,可以构造一个启发式估计函数h(.),该函数以任意精度均匀逼近最优估计函数h*(.)。本文描述了其中一种学习搜索算法a - b /sub - n/,结果表明,当先前问题的数量足够大时,a - b /sub - n/的最坏情况复杂度可降为O(poly(n)),其中n为最优解路径的长度,poly(n)是n的多项式函数。
In this paper, polynomial approximation method and theory are introduced into the research of learning search of artificial intelligence. By so doing, a learning search algorithm can, after sufficient number of problem-solving, construct a heuristic estimate function h(.) which uniformly approximates to the optimal estimate function h*(.) by arbitrary precision. One of these learning search algorithms, A-B/sub n/, is described and it is shown that, when the number of the previous problem-solving becomes large enough, the worst-case complexity of A-B/sub n/ can be reduced to O(poly(N)), where N is the length of the optimal solution path, poly(N) is a polynomial function of N.<>