Online learning of event definitions

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...

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Vydáno v:Theory and practice of logic programming Ročník 16; číslo 5-6; s. 817 - 833
Hlavní autoři: KATZOURIS, NIKOS, ARTIKIS, ALEXANDER, PALIOURAS, GEORGIOS
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cambridge, UK Cambridge University Press 01.09.2016
Témata:
Calculus
Distance education
Distance learning
Learning
Logic programming
Mathematical analysis
Recognition
Regular Papers
Strategy
Event Calculus
Inductive Logic Programming
Online Learning
ISSN:1471-0684, 1475-3081
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Shrnutí: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.
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ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068416000260