Query Learning Algorithm for Symbolic Weighted Finite Automata
Proceedings of the Fifteenth International Conference on Grammatical Inference, PMLR 153:202-216, 2021.
We propose a query learning algorithm for an extension of weighted finite automata (WFAs), named symbolic weighted finite automata (SWFAs), which can handle strings over infinite alphabets more efficiently. Based on the idea of symbolic finite automata, SWFAs generalize WFAs by allowing transitions to be functions from a possibly infinite alphabet to weights. Our algorithm can learn SWFAs if functions in transitions are also learnable by queries. We also investigate minimization and equivalence checking for SWFAs.