Artifacts using automata-util version 0.3.0

Infrastructure and core functionalities of LearnLib

A collection of basic algorithm for approximating equivalence queries

Functionality to ease writing test cases for LearnLib

A flexible, optimized version of Dana Angluin's L* algorithm

This artifact contains algorithms for incrementally constructing DFAs (prefix-closed and non-prefix-closed), Mealy machines, and Moore machines from a finite, incrementally growing set of example inputs/outputs.

This artifact provides the implementation of the DHC learning algorithm as described in the paper "Automata Learning with on-the-Fly Direct Hypothesis Construction" (https://doi.org/10.1007/978-3-642-34781-8_19) by Maik Merten, Falk Howar, Bernhard Steffen, and Tiziana Margaria.

A simple, straightforward implementation of Dana Angluin's L* algorithm

A collection of examples of how to use LearnLib

A collection of various small example applications that illustrate several use cases of AutomataLib.<br /> <em>Note:</em> This artifact is not intended as a library, but only exists for educational purposes.

This artifact contains several miscellaneous, well-known algorithms, which however are rather specific in their concrete use case and therefore not fit for the AutomataLib Utilities library. Examples include Dijkstra's algorithm for the SSSP problem, the Floyd-Warshall algorithm for the APSP problem and Tarjan's algorithm for finding all strongly-connected components in a graph.