论文标题
基于树的自适应模型学习
Tree-Based Adaptive Model Learning
论文作者
论文摘要
我们将Kearns-Vazirani学习算法扩展到能够处理随时间变化的系统。我们提出了一种新的学习算法，该算法可以重复使用和更新以前学习的行为，在LearnLib库中实现它，并在大型示例中对其进行评估，在该算法的两次运行中，我们对此进行了少量调整。在这些实验中，我们的算法显着优于经典的Kearns-Vazirani学习算法和当前最新的自适应算法。
We extend the Kearns-Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns-Vazirani learning algorithm and the current state-of-the-art adaptive algorithm.
