Multiple-Goal Heuristic Search

Details Multiple-Goal Heuristic Search Multiple-Goal Heuristic Search

2011-09-29

arxiv.org/abs/1109.6618v1

Journal Of Artificial Intelligence Research, Volume 26, pages 417-451, 2006

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.1940

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.

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