Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

• URL: http://arxiv.org/abs/2104.00158v1
• Date: Wed, 31 Mar 2021 23:16:29 GMT
• Title: Online Learning Probabilistic Event Calculus Theories in Answer Set Programming
• Authors: Nikos Katzouris, Alexander Artikis and Georgios Paliouras
• Abstract summary: Event Recognition (CER) systems detect occurrences in streaming time-stamped datasets using predefined event patterns. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of rules weighted in the Event Calculus. Our results demonstrate the superiority of our novel approach, both terms efficiency and predictive.
• Score: 70.06301658267125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 datasets 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).

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