arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.06790cs.FL
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自动机极小化在反应合成中的作用

On the Power of Automata Minimization in Reactive Synthesis

Shufang Zhu, Lucas M. Tabajara, Geguang Pu, Moshe Y. Vardi

摘要:

在人工智能应用中,时态逻辑经常被用来描述时态属性。最流行的语言是线性时态逻辑(Linear Temporal Logic,LTL)。最近,有限迹上的LTL,LTLf,已经在几种情况下进行了研究。为了对LTLf进行推理,通常将公式编译成确定性有限自动机(DFA),作为中间语义表示。此外,由于DFA具有规范表示的事实,可以应用有效的最小化算法来最大限度地减少DFA大小,从而有助于加速后续计算。在这里,我们提出了一个深入的调查两个经典的最小化算法,即Hopcroft和Brzozowski算法。更具体地说,我们展示了如何将这些算法应用于半符号(显式状态,符号转换函数)自动机表示。然后,我们比较这两种算法的上下文中的LTLf合成框架,从LTLf公式。虽然早期的研究比较两种算法从随机生成的自动机的结论是,没有算法占主导地位,我们的研究结果表明,从LTLf公式,Hopcroft的算法是最好的选择,在反应合成的背景下。更深入的分析解释了为什么Brzozowski算法的假定优势在实践中没有实现。

英文摘要:

Temporal logic is often used to describe temporal properties in AI applications. The most popular language for doing so is Linear Temporal Logic (LTL). Recently, LTL on finite traces, LTLf, has been investigated in several contexts. In order to reason about LTLf, formulas are typically compiled into deterministic finite automata (DFA), as the intermediate semantic representation. Moreover, due to the fact that DFAs have canonical representation, efficient minimization algorithms can be applied to maximally reduce DFA size, helping to speed up subsequent computations. Here, we present a thorough investigation on two classical minimization algorithms, namely, the Hopcroft and Brzozowski algorithms. More specifically, we show how to apply these algorithms to semi-symbolic (explicit states, symbolic transition functions) automata representation. We then compare the two algorithms in the context of an LTLf-synthesis framework, starting from LTLf formulas. While earlier studies on comparing the two algorithms starting from randomly-generated automata concluded that neither algorithm dominates, our results suggest that starting from LTLf formulas, Hopcroft's algorithm is the best choice in the context of reactive synthesis. Deeper analysis explains why the supposed advantage of Brzozowski's algorithm does not materialize in practice.

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。

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