Active automata learning (AAL) is a method to infer state machines by
interacting with black-box systems. Adaptive AAL aims to reduce the sample
complexity of AAL by incorporating domain specific knowledge in the form of
(similar) reference models. Such reference models appear naturally when
learning multiple versions or variants of a software system. In this paper, we
present state matching, which allows flexible use of the structure of these
reference models by the learner. State matching is the main ingredient of
adaptive L#, a novel framework for adaptive learning, built on top of L#. Our
empirical evaluation shows that adaptive L# improves the state of the art by up
to two orders of magnitude.

主题：主动自动机学习；适应性学习；状态匹配；参考模型；样本复杂度；摘要：本文介绍了一种自动学习的新框架，它通过状态匹配提供了对参考模型结构的灵活使用，从而减少了自动学习的样本复杂度。实证评估表明，该适应性学习框架改进了当前的状态艺术水平约两个数量级。

自适应主动自动机学习中的状态匹配和多重参考

State Matching and Multiple References in Adaptive Active Automata  Learning

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.

我们提出了一种主动自动机学习算法，它基于将个别输出进行投影从而学习一个有限状态机的分解。这与 Labbaf 等人 (2023) 提出的最近的组合学习算法互为对偶。当将输出投影到一个较小的集合时，模型本身也会缩小。通过有几个这样的投影，我们不会丢失任何信息，完整的系统可以被重建。根据系统的结构，这大大减少了查询的数量，如算法的初步评估所示。

输出分解学习 Mealy 机器

Output-decomposed Learning of Mealy Machines

Active automata learning algorithms cannot easily handle conflict in the
observation data (different outputs observed for the same inputs). This
inherent inability to recover after a conflict impairs their effective
applicability in scenarios where noise is present or the system under learning
is mutating. We propose the Conflict-Aware Active Automata Learning (C3AL)
framework to enable handling conflicting information during the learning
process. The core idea is to consider the so-called observation tree as a
first-class citizen in the learning process. Though this idea is explored in
recent work, we take it to its full effect by enabling its use with any
existing learner and minimizing the number of tests performed on the system
under learning, specially in the face of conflicts. We evaluate C3AL in a large
set of benchmarks, covering over 30 different realistic targets, and over
18,000 different scenarios. The results of the evaluation show that C3AL is a
suitable alternative framework for closed-box learning that can better handle
noise and mutations.

Conflicts in observation data pose a challenge for active automata learning algorithms, but the Conflict-Aware Active Automata Learning (C3AL) framework addresses this by considering the observation tree as a first-class citizen during the learning process, enabling better handling of noise and mutations.

冲突感知主动自动机学习

Conflict-Aware Active Automata Learning

Active automata learning algorithms cannot easily handle \emph{conflict} in
the observation data (different outputs observed for the same inputs). This
inherent inability to recover after a conflict impairs their effective
applicability in scenarios where noise is present or the system under learning
is mutating.
We propose the Conflict-Aware Active Automata Learning (C3AL) framework to
enable handling conflicting information during the learning process. The core
idea is to consider the so-called observation tree as a first-class citizen in
the learning process. Though this idea is explored in recent work, we take it
to its full effect by enabling its use with any existing learner and minimizing
the number of tests performed on the system under learning, specially in the
face of conflicts. We evaluate C3AL in a large set of benchmarks, covering over
30 different realistic targets, and over 18,000 different scenarios. The
results of the evaluation show that C3AL is a suitable alternative framework
for closed-box learning that can better handle noise and mutations.targets, and
over 18,000 different scenarios. The results of the evaluation show that C3AL
is a suitable alternative framework for closed-box learning that can better
handle noise and mutations.

冲突感知主动自动机学习 (C3AL) 框架是处理学习过程中冲突信息的合适替代框架，能够更好地处理噪声和变异的闭盒学习。