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Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Published online by Cambridge University Press:  01 August 2021

NIKOS KATZOURIS Open the ORCID record for NIKOS KATZOURIS [Opens in a new window] ,
GEORGIOS PALIOURAS  and
ALEXANDER ARTIKIS
NIKOS KATZOURIS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece (e-mail: [email protected], [email protected])
GEORGIOS PALIOURAS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece (e-mail: [email protected], [email protected])
ALEXANDER ARTIKIS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece Department of Maritime Studies, University of Piraeus, Piraeus, Greece (e-mail: [email protected])
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Abstract

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Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

Keywords

inductive logic programming and multi-relational data miningknowledge representation and nonmonotonic reasoning
Type
Rapid Communication
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 2 , March 2023 , pp. 362 - 386
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

This paper is an extended version of Katzouris and Artikis (2020), which has been nominated as a candidate for TPLP’s rapid publication track by KR2020’s program committee

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