仿射逼近的二元决策图
Binary Decision Diagrams for Affine Approximation
摘要：
Selman和Kautz关于“知识编译”的工作建立了如何近似（加强和/或削弱）命题知识库可以用来加速查询处理，以牺牲完整性为代价。在这种经典的方法中，查询使用给定知识库的Horn过近似和欠近似，该知识库表示为合取范式（CNF）中的命题公式。除了Horn函数类之外，人们还可以想象其他布尔函数类，它们可能具有相同的目的，因为它们具有与Horn函数类似的有吸引力的演绎计算性质。事实上，Zanuttini已经提出仿射布尔函数类在知识编译中可能是有用的，并提出了仿射近似算法。由于CNF是笨拙的仿射函数，Zanuttini认为这两个集的模型表示和使用模2同余方程。在本文中，我们提出了一种基于约简有序二叉决策图（ROBDDs）的算法。这导致了一个表示，这是更紧凑的模型集，一旦我们建立了一些有用的性质仿射布尔函数，一个更有效的算法。
英文摘要：
Selman and Kautz's work on ``knowledge compilation'' established how approximation (strengthening and/or weakening) of a propositional knowledge-base can be used to speed up query processing, at the expense of completeness. In this classical approach, querying uses Horn over- and under-approximations of a given knowledge-base, which is represented as a propositional formula in conjunctive normal form (CNF). Along with the class of Horn functions, one could imagine other Boolean function classes that might serve the same purpose, owing to attractive deduction-computational properties similar to those of the Horn functions. Indeed, Zanuttini has suggested that the class of affine Boolean functions could be useful in knowledge compilation and has presented an affine approximation algorithm. Since CNF is awkward for presenting affine functions, Zanuttini considers both a sets-of-models representation and the use of modulo 2 congruence equations. In this paper, we propose an algorithm based on reduced ordered binary decision diagrams (ROBDDs). This leads to a representation which is more compact than the sets of models and, once we have established some useful properties of affine Boolean functions, a more efficient algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
