Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

NIKOS KATZOURIS; GEORGIOS PALIOURAS; ALEXANDER ARTIKIS

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Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

机译：Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

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Complex Event Recognition (CER) systems detect event occurrences in streaming time-stampedinput 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 andchange, with machine learning, thus alleviating the cost of manual event pattern authoring. Wepresent a system based on Answer Set Programming (ASP), capable of probabilistic reasoningwith complex event patterns in the form of weighted rules in the Event Calculus, whose structureand weights are learnt online. We compare our ASP-based implementation with a Markov Logicbasedone and with a number of state-of-the-art batch learning algorithms on CER data setsfor activity recognition, maritime surveillance and fleet management. Our results demonstratethe 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).

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《Theory and practice of logic programming》 |2023年第2期|415-443|共29页
作者
NIKOS KATZOURIS; GEORGIOS PALIOURAS; ALEXANDER ARTIKIS;
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作者单位

Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR)'Demokritos', Athens, Greece;

Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR)'Demokritos', Athens, Greeceand Department of Maritime Studies, University of Piraeus, Piraeus, Greece;

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正文语种英语
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inductive logic programming and multi-relational data mining; knowledge representation and nonmonotonic reasoning;

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

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