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arXiv · 1705.02175

Distributed Online Learning of Event Definitions

Nikos Katzouris·Alexander Artikis·Georgios Paliouras
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Abstract

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. In this work we present a version of OLED that allows for distributed, online learning. We evaluate our approach on a benchmark activity recognition dataset and show that we can significantly reduce training times, exchanging minimal information between processing nodes.

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Nikos Katzouris, Alexander Artikis, Georgios Paliouras. 2017-05-05. Distributed Online Learning of Event Definitions. https://arxiv.org/abs/1705.02175

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