摘要
本文将多项式逼近的方法引入到学习式搜索中,使学习式搜索通过一定数量的解题训练后可以建立起一个任意一致逼近理想函数h(·)的启发估计函数h(·).本文给出了一个这样的学习式搜索算法A-Bn,并证明了当训练例子集充分大后,A-Bn可在多项式复杂度内解决任一后来提交的同类问题.
In this paper, Polynomial Approximation method and theory areintroduced into the rescarch of Learning Search of Artificial Intelligence. In this way,wecan use a search algorithm repeatedly to construct a heuristic estimate function h(.)which uniformly approximates to the optimal estimate function h(.)with arbitrarily high precision. One of such learning search algorithms,A-Bn,ispresented and it is shown that, when the number of training samples becomes largeenough,the worst-case complexity of A-Bn can be reduced to O(poly(N)),where Nis the length of the optimal solution path,poly(N) is a ploymomial of N.
出处
《辽宁大学学报(自然科学版)》
CAS
1995年第A00期113-116,122,共5页
Journal of Liaoning University:Natural Sciences Edition
关键词
复杂度
多项式逼近
学习式搜索
函数逼近
Artificial Intelligence
Heuristic Search
Machine Learning
Complexity.