论文标题
在答案集编程中学习基于自动机的复杂事件模式
Learning Automata-Based Complex Event Patterns in Answer Set Programming
论文作者
论文摘要
复杂的事件识别和预测（CER/F）技术试图使用预定义事件模式在流输入中提前检测甚至预测。这种模式并不总是事先知道，或者它们经常随着时间的流逝而变化，从而使机器学习技术能够从数据中提取此类模式，在CER/F中非常需要。由于许多CER/F系统使用符号自动机代表这种模式，因此我们提出了一个自动机的家族，其中通过答案集编程（ASP）规则来定义了实现过渡的条件，并且由于ASP与符号学习与符号学习的牢固联系，可以直接从数据中学习。我们在ASP及其增量版本中介绍了这种学习方法，该方法以效率为效率，并能够扩展到大型数据集。我们在两个CER数据集上评估了我们的方法，并将其与最先进的自动机学习技术进行了比较，从经验上讲，在预测的准确性和可伸缩性方面都表现出了卓越的性能。
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.
