为自动化学习工具AALpy新增被动学习的通用状态合并方法。
Extending AALpy with Passive Learning: A Generalized State-Merging Approach
- 基于红蓝框架统一表示，实现跨类型自动机的状态合并。
- 仅需定义兼容性与评分规则，即可快速实现新旧算法。
- 适合自动化学习研究者和需要高效实现算法的开发者。
AALpy 是一个以主动学习为核心、专注于输入输出行为系统建模的开源自动化学习库，支持从完全确定性到概率型自动机等多种类型。本文介绍了其最新扩展：在被动学习领域引入一种通用的状态合并方法——红蓝框架下的状态合并。通过采用统一的内部表示形式，实现了对不同自动机类型的通用且高度可配置的红蓝框架支持。文中详细说明了如何利用 AALpy 定义并执行状态合并算法，显著降低了实现成本，主要工作集中在定义兼容性标准与评分函数。借助该机制，实现已有或新型状态合并算法只需数行代码，极大提升了开发效率。
原文摘要 · Abstract (English)
AALpy is a well-established open-source automata learning library written in Python with a focus on active learning of systems with IO behavior. It provides a wide range of state-of-the-art algorithms for different automaton types ranging from fully deterministic to probabilistic automata. In this work, we present the recent addition of a generalized implementation of an important method from the domain of passive automata learning: state-merging in the red-blue framework. Using a common internal representation for different automaton types allows for a general and highly configurable implementation of the red-blue framework. We describe how to define and execute state-merging algorithms using AALpy, which reduces the implementation effort for state-merging algorithms mainly to the definition of compatibility criteria and scoring. This aids the implementation of both existing and novel algorithms. In particular, defining some existing state-merging algorithms from the literature with AALpy only takes a few lines of code.
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