scholarly journals Exploiting Inductive Logic Programming Techniques for Declarative Process Mining

2009 ◽  
pp. 278-295 ◽  
Author(s):  
Federico Chesani ◽  
Evelina Lamma ◽  
Paola Mello ◽  
Marco Montali ◽  
Fabrizio Riguzzi ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Process Mining ◽  
Inductive Logic ◽  
Programming Techniques

Applying Inductive Logic Programming to Process Mining

2008 ◽  
pp. 132-146 ◽  
Author(s):  
Evelina Lamma ◽  
Paola Mello ◽  
Fabrizio Riguzzi ◽  
Sergio Storari
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Process Mining ◽  
Inductive Logic

scholarly journals Online learning of event definitions

2016 ◽  
Vol 16 (5-6) ◽  
pp. 817-833 ◽  
Author(s):  
NIKOS KATZOURIS ◽  
ALEXANDER ARTIKIS ◽  
GEORGIOS PALIOURAS
Keyword(s):  
Online Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Learning Strategy ◽  
Inductive Logic ◽  
Event Recognition ◽  
Event Calculus ◽  
Training Time ◽  
Learning Techniques ◽  
Programming Techniques

AbstractSystems 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.

ILPNET repositories on WWW: Inductive Logic Programming systems, datasets and bibliography

1996 ◽  
Vol 9 (4) ◽  
pp. 157-206 ◽  
Author(s):  
Nada Lavrač ◽  
Irene Weber ◽  
Darko Zupanič ◽  
Dimitar Kazakov ◽  
Olga Štěpánková ◽  
...  
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Programming Systems
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