The complexity of learning linear temporal formulas from examples

被引：0
|
作者：
Fijalkow, Nathanael [1 ,2 ]
Lagarde, Guillaume [1 ]
机构：
[1] Univ Bordeaux, LaBRI, CNRS, Bordeaux, France
[2] Alan Turing Inst, London, England
来源：
INTERNATIONAL CONFERENCE ON GRAMMATICAL INFERENCE, VOL 153 | 2021年 / 153卷
关键词：
passive learning; automata learning; linear temporal logic; approximation algorithms;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we initiate the study of the computational complexity of learning linear temporal logic (LTL) formulas from examples. We construct approximation algorithms for fragments of LTL and prove hardness results; in particular we obtain tight bounds for the fragment containing only the next operator and conjunctions, and prove NP-hardness results for many fragments.
引用
收藏
页码：237 / 250
页数：14
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