libalf: The Automata Learning Framework

d Abstract) Benedikt Bollig1 , Joost-Pieter Katoen2 , Carsten Kern2 , Martin Leucker3 , Daniel Neider2 , and David R. Piegdon2 1 LSV, ENS Cachan, CNRS, 2 RWTH Aachen University, 3 TU M¨unchen This paper presents libalf, a comprehensive, open-source library for learning finite-state automata covering various well-known learning techniques (such as, Angluin’s L∗ , Biermann, and RPNI, as well as a novel learning algorithm for NFA. libalf is highly flexible and allows for facilely interchanging learning algorithms and combining domain-specific features in a plug-andplay fashion. Its modular design and its implementation in C++ make it the ideal platform for adding and engineering further, efficient learning algorithms for new target models (e.g., B¨uchi automata).

• Type: Conference
• Year: 2010
• Where: CAV
• Authors: Benedikt Bollig, Joost-Pieter Katoen, Carsten Kern, Martin Leucker, Daniel Neider, David R. Piegdon
• Keywords: CAV 2010, Efficient Learning Algorithms, Hardware, Learning Algorithms, Well-known Learning Techniques