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Degeneralization Algorithm for Generation of Büchi Automata Based on Contented Situation

机译:基于知足情况的布奇自动机生成的去矿化算法

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We present on-the-fly degeneralization algorithm used to transform generalized Büchi automata (GBA) into Büchi Automata (BA) different from the standard degeneralization algorithm. Contented situation, which is used to record what acceptance conditions are satisfiable during expanding LTL formulae, is attached to the states and transitions in the BA. In order to get the deterministic BA, the Shannon expansion is used recursively when we expand LTL formulae by applying the tableau rules. On-the-fly degeneralization algorithm is carried out in each step of the expansion of LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae. The temporary automata are stored as syntax directed acyclic graph in order to save storage space. These ideas are implemented in a conversion algorithm used to build a property automaton corresponding to the given LTL formulae. We compare our method to previous work and show that it is more efficient for four sets of random formulae generated by LBTT.
机译:我们提出了一种动态去离子化算法,该算法用于将广义的Büchi自动机(GBA)转换为BüchiAutomata(BA),与标准的去矿化算法不同。满足情况,用于记录在扩展LTL公式期间可以满足的接受条件,该情况与BA中的状态和转换有关。为了获得确定性BA,当我们通过应用表格规则扩展LTL公式时,将递归使用Shannon扩展。在扩展LTL公式的每个步骤中,都会进行即时的去矿化算法。有序二进制决策图用于表示BA并简化LTL公式。临时自动机存储为语法定向的非循环图,以节省存储空间。这些思想在用于构建与给定LTL公式相对应的属性自动机的转换算法中实现。我们将我们的方法与以前的工作进行了比较,并表明该方法对于LBTT生成的四组随机公式更为有效。

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