Learning Automata-Based Complex Event Patterns in Answer Set Programming

TL;DR

This paper introduces a novel automata learning method for complex event recognition that uses Answer Set Programming rules, enabling scalable and accurate pattern detection in streaming data.

Contribution

It proposes a new family of automata with ASP-defined transitions, and an incremental learning approach that scales efficiently to large datasets.

Findings

Outperforms state-of-the-art automata learning techniques in accuracy

Demonstrates superior scalability on large datasets

Effective in real-world CER datasets

Abstract

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.