Active Automata Learning from Noisy Data - Algorithms and Artifacts
Active Automata Learning from Noisy Data - Algorithms and Artifacts
This repository contains the benchmarking set, evaluation results and source code of the implementation for the Active Partial Max-SAT Learning (APMSL). It also contains the passive-to-active learning framework to make passive automata learning algorithms active together with all other artifacts presented in the paper "Active Automata Learning with Noisy Data: From Big to Small Data" by Felix Wallner, Bernhard Aichernig, Benjamin von Berg and Maximilian Rindler accepted to the FM 2026 Conference. It also contains a pre-built multi-architecture docker image for ease of use. Additionally, the APMSL algorithm is actively maintained on gitlab.com/felixwallner/apmsl
Model Learning, Grammar Inference, Active Automata Learning, Partial Max-SAT, Noise-tolerant, Passive Automata Learning
Model Learning, Grammar Inference, Active Automata Learning, Partial Max-SAT, Noise-tolerant, Passive Automata Learning
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