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.

翻译：我们提出一种基于输出投影的主动自动机学习算法，用于学习有限状态机的分解。该方法与Labbaf等人（2023）近期提出的组合学习算法具有对偶性。当将输出投影到更小的集合时，模型本身的规模会相应缩减。通过保留多个此类投影，我们不会丢失任何信息，且完整系统可被重构。初步算法评估表明，根据系统结构的不同，该方法可大幅减少查询次数。