Gray-Box Learning of Serial Compositions of Mealy Machines

被引：9
|
作者：
Abel, Andreas [1 ]
Reineke, Jan [1 ]
机构：
[1] Univ Saarland, Dept Comp Sci, Saarbrucken, Germany
来源：
NASA FORMAL METHODS, NFM 2016 | 2016年 / 9690卷
关键词：
INFERENCE;
D O I：
10.1007/978-3-319-40648-0_21
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
We study the following gray-box learning problem: Given the serial composition of two Mealy machines A and B, where A is known and B is unknown, the goal is to learn a model of B using only output and equivalence queries on the composed machine. We introduce an algorithm that solves this problem, using at most vertical bar B vertical bar equivalence queries, independently of the size of A. We discuss its efficient implementation and evaluate the algorithm on existing benchmark sets as well as randomly-generated machines.
引用
收藏
页码：272 / 287
页数：16
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