Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5247-5256, 2018.
We present a novel algorithm that uses exact learning and abstraction to extract a deterministic finite automaton describing the state dynamics of a given trained RNN. We do this using Angluin’s \lstar algorithm as a learner and the trained RNN as an oracle. Our technique efficiently extracts accurate automata from trained RNNs, even when the state vectors are large and require fine differentiation.