License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2023.17
URN: urn:nbn:de:0030-drops-191071
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19107/
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Katzouris, Nikos ; Paliouras, Georgios

Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition (Extended Abstract)

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LIPIcs-TIME-2023-17.pdf (0.4 MB)

Abstract

Complex Event Recognition (CER) systems detect event occurrences in streaming input using predefined event patterns. Techniques that learn event patterns from data are highly desirable in CER. Since such patterns are typically represented by symbolic automata, 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 learnable from data. We present such a learning approach in ASP, capable of jointly learning the structure of an automaton and its transition guards' definitions from building-block predicates, and a scalable, incremental version thereof that progressively revises models learnt from mini-batches using Monte Carlo Tree Search. We evaluate our approach on three CER datasets and empirically demonstrate its efficacy.

BibTeX - Entry

@InProceedings{katzouris_et_al:LIPIcs.TIME.2023.17,
  author =	{Katzouris, Nikos and Paliouras, Georgios},
  title =	{{Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{17:1--17:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19107},
  URN =		{urn:nbn:de:0030-drops-191071},
  doi =		{10.4230/LIPIcs.TIME.2023.17},
  annote =	{Keywords: Event Pattern Learning, Answer Set Programming}
}

Keywords: Event Pattern Learning, Answer Set Programming
Collection: 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)
Issue Date: 2023
Date of publication: 18.09.2023

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