Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
arXiv:1711.09576
Abstract
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 L* 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.
Accepted in ICML 2018, (Feb 2020: added link to code, at https://github.com/tech-srl/lstar_extraction )