Complex Event Recognition and Forecasting (CER/F) techniques attempt to detect, or even forecast ahead of time, event occurrences in streaming input using predefined event patterns. Such patterns are not always known in advance, or they frequently change over time, making machine learning techniques, capable of extracting such patterns from data, highly desirable in CER/F. Since many CER/F systems use symbolic automata to represent such patterns, we propose a family of such automata where the transition-enabling conditions are defined by Answer Set Programming (ASP) rules, and which, thanks to the strong connections of ASP to symbolic learning, are directly learnable from data. We present such a learning approach in ASP and an incremental version thereof that trades optimality for efficiency and is capable to scale to large datasets. We evaluate our approach on two CER datasets and compare it to state-of-the-art automata learning techniques, demonstrating empirically a superior performance, both in terms of predictive accuracy and scalability.

翻译：由于许多CER/F系统使用象征性的自动数据组来代表这种模式,我们建议采用这种自动数据组技术的组合,在这种系统中,过渡-授权条件由问答组程序制定规则确定,由于ASP与象征性学习的紧密联系,可以直接从数据中学习。我们在ASP中介绍了这种学习方法及其递增版本,这种学习方法可以使效率达到最佳程度,并且能够对大型数据集进行规模化。我们评估了我们对两个CER数据集采用的方法,并将其与最新自动数据学习技术进行比较,从经验上表明在预测准确性和可缩放性两方面都表现优异。