基于多项式近似的学习搜索

Wei Zhang, Shenggui Hong
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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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polynomial approximation based learning search
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.<>
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