scholarly journals Multi-Automata Learning

10.5772/5280 ◽  
2008 ◽  
Author(s):  
Verbeeck Katja ◽  
Nowe Ann ◽  
Vrancx Peter ◽  
Peeters Maarte
Keyword(s):  
Automata Learning

Synthesize Models for Quantitative Analysis Using Automata Learning

2019 ◽  
pp. 75-92
Author(s):  
Yu-Fang Chen ◽  
Hsiao-Chen Chung ◽  
Wen-Chi Hung ◽  
Ming-Hsien Tsai ◽  
Bow-Yaw Wang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Automata Learning

scholarly journals Compositional learning of mutually recursive procedural systems

2021 ◽  
Author(s):  
Markus Frohme ◽  
Bernhard Steffen
Keyword(s):  
Finite Automata ◽  
Simultaneous Inference ◽  
Recursive Structure ◽  
Recursive Models ◽  
Membership Queries ◽  
Compositional Approach ◽  
Recursive Programs ◽  
Automata Learning ◽  
The Individual ◽  
Compositional Learning

AbstractThis paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.

scholarly journals Single Image Haze Removal Using Deep Cellular Automata Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 103181-103199
Author(s):  
Surasak Tangsakul ◽  
Sartra Wongthanavasu
Keyword(s):  
Cellular Automata ◽  
Single Image ◽  
Haze Removal ◽  
Automata Learning

Dynamic testing via automata learning

2009 ◽  
Vol 11 (4) ◽  
pp. 307-324 ◽  
Author(s):  
Harald Raffelt ◽  
Maik Merten ◽  
Bernhard Steffen ◽  
Tiziana Margaria
Keyword(s):  
Dynamic Testing ◽  
Automata Learning

scholarly journals Enhancing Automata Learning by Log-Based Metrics

2016 ◽  
pp. 295-310 ◽  
Author(s):  
Petra van den Bos ◽  
Rick Smetsers ◽  
Frits Vaandrager
Keyword(s):  
Automata Learning
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