3 research outputs found
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
We present a method to extract a weighted finite automaton (WFA) from a
recurrent neural network (RNN). Our algorithm is based on the WFA learning
algorithm by Balle and Mohri, which is in turn an extension of Angluin's
classic \lstar algorithm. Our technical novelty is in the use of
\emph{regression} methods for the so-called equivalence queries, thus
exploiting the internal state space of an RNN to prioritize counterexample
candidates. This way we achieve a quantitative/weighted extension of the recent
work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally
evaluate the accuracy, expressivity and efficiency of the extracted WFAs.Comment: AAAI 2020. We are preparing to distribute the implementatio
Genetic Algorithm for the Weight Maximization Problem on Weighted Automata
The weight maximization problem (WMP) is the problem of finding the word of
highest weight on a weighted finite state automaton (WFA). It is an essential
question that emerges in many optimization problems in automata theory.
Unfortunately, the general problem can be shown to be undecidable, whereas its
bounded decisional version is NP-complete. Designing efficient algorithms that
produce approximate solutions to the WMP in reasonable time is an appealing
research direction that can lead to several new applications including formal
verification of systems abstracted as WFAs. In particular, in combination with
a recent procedure that translates a recurrent neural network into a weighted
automaton, an algorithm for the WMP can be used to analyze and verify the
network by exploiting the simpler and more compact automata model. In this
work, we propose, implement and evaluate a metaheuristic based on genetic
algorithms to approximate solutions to the WMP. We experimentally evaluate its
performance on examples from the literature and show its potential on different
applications.Comment: Accepted at GECCO 202