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.
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Notes
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Maximizing earliness for input rejection, in addition to acceptance, would also be possible by modifying the ASA interpreter to not handle rejection via the closed-world assumption, but via explicit, absorbing dead states, and adding appropriate regularization constraints.
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Acknowledgment
This work is supported by the project entitled “ARIADNE - AI Aided D-band Network for 5G Long Term Evolution”, which has received funding from the European Union’s Horizon 2020 research & innovation programme under grant agreement No 871464, and by the project entitled “INFORE: Interactive Extreme-Scale Analytics and Forecasting”, which has received funding from the European Union’s Horizon 2020 research & innovation programme under grant agreement No 825070.
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Katzouris, N., Paliouras, G. (2024). Learning Automata-Based Complex Event Patterns in Answer Set Programming. In: Muggleton, S.H., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2022. Lecture Notes in Computer Science(), vol 13779. Springer, Cham. https://doi.org/10.1007/978-3-031-55630-2_5
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