Multiple-Goal Heuristic Search

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.