期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2006
卷号：26
页码：417-451
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources.
We show the inadequacy of traditional distance-estimation heuristics for
tasks of this type and present alternative heuristics that are more
appropriate for multiple-goal search. In particular, we introduce the
marginal-utility heuristic, which estimates the cost and the benefit of
exploring a subtree below a search node. We developed two methods for
online learning of the marginal-utility heuristic. One is based on
local similarity of the partial marginal utility of sibling nodes, and the
other generalizes marginal-utility over the state feature space. We apply our
adaptive and non-adaptive multiple-goal search algorithms to several
problems, including focused crawling, and show their superiority over
existing methods.
