摘要翻译：
我们考虑使用一组加权规则计算全局结构的最轻推导的问题。人工智能中的许多推理问题都可以在这个框架中得到解决。我们将从抽象中导出的a*搜索和启发式推广到一类最轻的导出问题。我们还描述了一种新的算法，它使用抽象层次来搜索最轻的派生。我们对a*的推广给出了一种以自底向上的方式搜索和/或图的新算法。我们讨论这里描述的算法如何为解决管道问题提供一个通用的架构--在感知系统的不同处理阶段之间来回传递信息的问题。我们考虑计算机视觉和自然语言处理方面的例子。将分层搜索算法应用于灰度图像中凸目标的边界估计问题，并与其他搜索方法进行了比较。第二组实验展示了一种新的组合模型用于寻找图像中的显著曲线。
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英文标题：
《The Generalized A* Architecture》
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作者：
P. F. Felzenszwalb, D. McAllester
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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：
  We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A* search and heuristics derived from abstractions to a broad class of lightest derivation problems. We also describe a new algorithm that searches for lightest derivations using a hierarchy of abstractions. Our generalization of A* gives a new algorithm for searching AND/OR graphs in a bottom-up fashion. We discuss how the algorithms described here provide a general architecture for addressing the pipeline problem --- the problem of passing information back and forth between various stages of processing in a perceptual system. We consider examples in computer vision and natural language processing. We apply the hierarchical search algorithm to the problem of estimating the boundaries of convex objects in grayscale images and compare it to other search methods. A second set of experiments demonstrate the use of a new compositional model for finding salient curves in images.
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PDF链接：
https://arxiv.org/pdf/1110.2216