Output-decomposed Learning of Mealy Machines
This work addresses efficiency in automata learning for researchers and practitioners, but it appears incremental as it builds on a recent compositional approach.
The paper tackles the problem of learning finite state machines more efficiently by decomposing them based on individual outputs, which reduces model size and query count. Preliminary evaluation shows a drastic reduction in the number of queries required.
We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do not lose any information and the full system can be reconstructed. Depending on the structure of the system this reduces the number of queries drastically, as shown by a preliminary evaluation of the algorithm.