摘要翻译：
本文提出了一种新的随时随地启发式搜索框架，其任务是在分配的资源内实现尽可能多的目标。我们指出了传统的距离估计启发式在这类任务中的不足，并提出了更适合于多目标搜索的替代启发式。特别地，我们引入了边际效用启发式，它估计了搜索节点下面的子树的成本和收益。我们开发了两种边际效用启发式在线学习方法。一种是基于兄弟节点局部边际效用的局部相似性，另一种是在状态特征空间上推广边际效用。我们将我们的自适应和非自适应多目标搜索算法应用于几个问题，包括聚焦爬行，并显示了它们相对于现有方法的优越性。
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英文标题：
《Multiple-Goal Heuristic Search》
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作者：
D. Davidov, S. Markovitch
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1109.6618