
TL;DR
This paper introduces a new framework for multiple-goal heuristic search, emphasizing the marginal-utility heuristic to better estimate the benefit of exploring subtrees, with adaptive learning methods demonstrating superior performance.
Contribution
It presents the marginal-utility heuristic and two online learning methods, advancing the effectiveness of anytime heuristic search for multiple goals.
Findings
Marginal-utility heuristic outperforms traditional heuristics.
Adaptive learning improves search efficiency.
Methods applied successfully to focused crawling.
Abstract
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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