تقرير
Online Learning of Event Definitions
| العنوان: | Online Learning of Event Definitions |
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| المؤلفون: | Katzouris, Nikos, Artikis, Alexander, Paliouras, Georgios |
| المصدر: | Theory and Practice of Logic Programming 16(5-6), 817-833, 2016 |
| سنة النشر: | 2016 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | Systems for symbolic event recognition 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). We present an ILP system for online learning of Event Calculus theories. To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream. We employ a decoupling scheme of the Event Calculus axioms during the learning process, that allows to learn each clause in isolation. Moreover, we use abductive-inductive logic programming techniques to handle unobserved target predicates. We evaluate our approach on an activity recognition application and compare it to a number of batch learning techniques. We obtain results of comparable predicative accuracy with significant speed-ups in training time. We also outperform hand-crafted rules and match the performance of a sound incremental learner that can only operate on noise-free datasets. This paper is under consideration for acceptance in TPLP. Comment: Paper presented at the 32nd International Conference on Logic Programming (ICLP 2016), New York City, USA, 16-21 October 2016, 15 pages, LaTeX, 1 PDF figure |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1017/S1471068416000260 |
| URL الوصول: | http://arxiv.org/abs/1608.00100 |
| رقم الأكسشن: | edsarx.1608.00100 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1017/S1471068416000260 |
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