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arXiv 2408.04486cs.LO
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学习时序性质的复杂性

The Complexity of Learning Temporal Properties

Benjamin Bordais, Daniel Neider, Rajarshi Roy

AI总结:

本文研究LTL、CTL和ATL时序逻辑被动学习问题的计算复杂性,证明无界二元运算符情况下均为NP完全,并揭示有界情况下不同逻辑间的复杂性差异。

摘要:

我们考虑从系统行为示例中学习时序逻辑公式的问题。学习时序性质已成为解释复杂时序行为的一种有效手段。目前已设计了若干高效算法用于学习时序公式。然而,对学习决策问题复杂性的理论理解在很大程度上仍未得到探索。为了解决这一问题,我们研究了三种重要时序逻辑——线性时序逻辑(LTL)、计算树逻辑(CTL)和交替时序逻辑(ATL)及其若干片段的被动学习问题的复杂性。我们证明,对于所有这些逻辑,使用无界数量的二元运算符出现次数来学习公式是NP完全的。另一方面,在研究具有有界二元运算符出现次数的公式学习复杂性时,我们展示了学习LTL、CTL和ATL公式(具有不同数量的智能体)的复杂性之间的差异。

英文摘要:

We consider the problem of learning temporal logic formulas from examples of system behavior. Learning temporal properties has crystallized as an effective mean to explain complex temporal behaviors. Several efficient algorithms have been designed for learning temporal formulas. However, the theoretical understanding of the complexity of the learning decision problems remains largely unexplored. To address this, we study the complexity of the passive learning problems of three prominent temporal logics, Linear Temporal Logic (LTL), Computation Tree Logic (CTL) and Alternating-time Temporal Logic (ATL) and several of their fragments. We show that learning formulas using an unbounded amount of occurrences of binary operators is NP-complete for all of these logics. On the other hand, when investigating the complexity of learning formulas with bounded amount of occurrences of binary operators, we exhibit discrepancies between the complexity of learning LTL, CTL and ATL formulas (with a varying number of agents).

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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