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P2 Pushing for weighted tree automata

面向加权树自动机的 pushing
LMCS vol.Volume 14, Issue 12018匹配度 2/10 · 论文研究加权树自动机的 pushing 归一化、最小化与等价性测试,属于纯自动机理论,与用户关注的 PL/AI 交叉、LLM/Agent、浮点程序分析、贝叶斯程序分析均无直接关联;仅在形式化验证/自动机理论基础层面有非常间接的潜在联系。引用 1 inbox
Thomas Hanneforth, Andreas Maletti, Daniel Quernheim
crossref: 2026-09-10

AI 速览

一句话:提出加权树自动机的权重归一化(pushing),用于最小化与等价性测试,并显著降低等价性测试复杂度。

本文为定义在交换半域上的加权树自动机引入一种称为 pushing 的权重归一化过程。该过程在非确定型加权树自动机上也保持所识别的加权树语言不变,但对自底向上确定型自动机尤其有用:通过精心选择需要重新分配的权重并进行归一化,可把最小化和等价性测试归约到相应的自底向上确定型无权树自动机问题。作者据此给出新的等价性测试,运行时间为 O((|M|+|M'|)·log(|Q|+|Q'|)),改进了此前 O(|M|·|M'|) 的最好界。

问题
如何对加权树自动机进行权重归一化,并用它简化自底向上确定型加权树自动机的最小化与等价性测试,同时改进等价性测试的复杂度。
方法
引入 pushing 权重归一化,保持所识别的加权树语言;通过选择权重重分配,将问题归约到自底向上确定型无权树自动机;并设计新的等价性测试算法。
评估
以理论复杂度分析评估:新等价性测试时间为 O((|M|+|M'|)·log(|Q|+|Q'|)),优于先前 O(|M|·|M'|) 的最好界。
原文摘要(Abstract)

A weight normalization procedure, commonly called pushing, is introduced for weighted tree automata (wta) over commutative semifields. The normalization preserves the recognized weighted tree language even for nondeterministic wta, but it is most useful for bottom-up deterministic wta, where it can be used for minimization and equivalence testing. In both applications a careful selection of the weights to be redistributed followed by normalization allows a reduction of the general problem to the corresponding problem for bottom-up deterministic unweighted tree automata. This approach was already successfully used by Mohri and Eisner for the minimization of deterministic weighted string automata. Moreover, the new equivalence test for two wta $M$ and $M'$ runs in time $\mathcal O((\lvert M \rvert + \lvert M'\rvert) \cdot \log {(\lvert Q\rvert + \lvert Q'\rvert)})$, where $Q$ and $Q'$ are the states of $M$ and $M'$, respectively, which improves the previously best run-time $\mathcal O(\lvert M \rvert \cdot \lvert M'\rvert)$.

链接与引用

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BibTeX
@article{paperbot437,
  title = {Pushing for weighted tree automata},
  author = {Thomas Hanneforth and Andreas Maletti and Daniel Quernheim},
  journal = {Logical Methods in Computer Science},
  volume = {Volume 14, Issue 1},
  year = {2018},
  doi = {10.23638/lmcs-14(1:5)2018}
}