Learning Automata-Based Complex Event Patterns in Answer Set Programming
This addresses the need for adaptive pattern recognition in CER/F systems where patterns are unknown or changing, offering a scalable solution for event forecasting.
The paper tackled the problem of learning complex event patterns from streaming data by proposing automata with Answer Set Programming (ASP) rules as transition conditions, which are directly learnable. It demonstrated superior predictive accuracy and scalability compared to state-of-the-art techniques on two CER datasets.
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.