首页> 外文会议>Australasian joint conference on artificial intelligence >A New Efficient In Situ Sampling Model for Heuristic Selection in Optimal Search
【24h】

A New Efficient In Situ Sampling Model for Heuristic Selection in Optimal Search

机译:最优搜索中启发式选择的新型高效原位采样模型

获取原文

摘要

Techniques exist that enable problem-solvers to automatically generate an almost unlimited number of heuristics for any given problem. Since they are generated for a specific problem, the cost of selecting a heuristic must be included in the cost of solving the problem. This involves a tradeoff between the cost of selecting the heuristic and the benefits of using that specific heuristic over using a default heuristic. The question we investigate in this paper is how many heuristics can we handle when selecting from a large number of heuristics and still have the benefits outweigh the costs. The techniques we present in this paper allow our system to handle several million candidate heuristics.
机译:存在使问题解决者能够针对任何给定问题自动生成几乎无限数量的启发式技术的技术。由于它们是针对特定问题生成的,因此选择启发式方法的成本必须包含在解决问题的成本中。这涉及在选择试探法的成本与使用特定试探法的收益与使用默认试探法的收益之间的权衡。我们在本文中研究的问题是,在从大量启发式方法中进行选择时,我们可以处理多少种启发式方法,而其收益仍大于成本。我们在本文中介绍的技术使我们的系统能够处理数百万种候选启发式方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号