2026-07-25T03:14:33Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:19107 2024-03-06T11:03:12Z ddc:004 open_access

Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition (Extended Abstract) Katzouris, Nikos Paliouras, Georgios Event Pattern Learning Answer Set Programming 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nikos Katzouris and Georgios Paliouras 2023 Is Part Of LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.TIME.2023.17 urn:nbn:de:0030-drops-191071 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.17 eng https://creativecommons.org/licenses/by/4.0/legalcode