Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-28T22:29:59.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Online learning of event definitions

Published online by Cambridge University Press:  14 October 2016

NIKOS KATZOURIS ,
ALEXANDER ARTIKIS  and
GEORGIOS PALIOURAS
NIKOS KATZOURIS
Affiliation:
Department of Informatics & Telecommunications, National Kapodistrian University of Athens, Athens, Greece (e-mail: nkatz@iit.demokritos.gr) Institute of Informatics & Telecommunications, National Center for Scientific Research “Demokritos”, Athens, Greece (e-mail: paliourg@iit.demokritos.gr)
ALEXANDER ARTIKIS
Affiliation:
Department of Maritime Studies, University of Piraeus, Piraeus, Greece (e-mail: a.artikis@iit.demokritos.gr) Institute of Informatics & Telecommunications, National Center for Scientific Research “Demokritos”, Athens, Greece (e-mail: paliourg@iit.demokritos.gr)
GEORGIOS PALIOURAS
Affiliation:
Institute of Informatics & Telecommunications, National Center for Scientific Research “Demokritos”, Athens, Greece (e-mail: paliourg@iit.demokritos.gr)
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

Inductive Logic ProgrammingEvent CalculusOnline Learning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Special Issue 5-6: 32nd International Conference on Logic Programming , September 2016 , pp. 817 - 833
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Artikis, A., Sergot, M. and Paliouras, G. 2015. An event calculus for event recognition. Knowledge and Data Engineering, IEEE Transactions on 27, 4, 895–908.CrossRefGoogle Scholar
Artikis, A., Skarlatidis, A. and Paliouras, G. 2010. Behaviour recognition from video content: a logic programming approach. International Journal on Artificial Intelligence Tools 19, 02, 193–209.CrossRefGoogle Scholar
Athakravi, D., Corapi, D., Broda, K. and Russo, A. 2013. Learning through hypothesis refinement using answer set programming. In Inductive Logic Programming, Springer, 31–46.Google Scholar
Blockeel, H. and De Raedt, L. 1998. Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1, 285–297.CrossRefGoogle Scholar
Blockeel, H., De Raedt, L., Jacobs, N. and Demoen, B. 1999. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3, 1, 59–93.CrossRefGoogle Scholar
De Raedt, L. 2008. Logical and Relational Learning. Springer Science & Business Media.CrossRefGoogle Scholar
Denecker, M. and Kakas, A. 2002. Abduction in logic programming. In Computational Logic: Logic Programming and Beyond. Springer, 402–436.CrossRefGoogle Scholar
Dhurandhar, A. and Dobra, A. 2012. Distribution-free bounds for relational classification. Knowledge and information systems 31, 1, 55–78.CrossRefGoogle Scholar
Domingos, P. and Hulten, G. 2000. Mining high-speed data streams. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 71–80.CrossRefGoogle Scholar
Domingos, P. and Hulten, G. 2001. A general method for scaling up machine learning algorithms and its application to clustering. In ICML, Vol. 1. 106–113.Google Scholar
Esposito, F., Semeraro, G., Fanizzi, N. and Ferilli, S. 2000. Multistrategy theory revision: Induction and abduction in inthelex. Machine Learning 38, 1–2, 133–156.CrossRefGoogle Scholar
Etzion, O. and Niblett, P. 2010. Event Processing in Action. Manning Publications Co.Google Scholar
Gama, J. 2010. Knowledge Discovery from Data Streams. CRC Press.CrossRefGoogle Scholar
Gama, J. and Gaber, M. M. 2007. Learning from Data Streams. Springer.CrossRefGoogle Scholar
Gama, J., Kosina, P., et al. 2011. Learning decision rules from data streams. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, Vol. 22. Citeseer, 1255.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2012. Answer set solving in practice. Synthesis Lectures on Artificial Intelligence and Machine Learning 6, 3, 1–238.CrossRefGoogle Scholar
Hoeffding, W. 1963. Probability inequalities for sums of bounded random variables. Journal of the American statistical association 58, 301, 13–30.CrossRefGoogle Scholar
Hulten, G., Domingos, P. and Abe, Y. 2003. Mining massive relational databases. In Proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data. 53–60.Google Scholar
Huynh, T. N. and Mooney, R. J. 2009. Max-margin weight learning for markov logic networks. In Machine Learning and Knowledge Discovery in Databases. Springer, 564–579.CrossRefGoogle Scholar
Jensen, D. 1999. Statistical challenges to inductive inference in linked data. In AISTATS.Google Scholar
Jensen, D. and Neville, J. 2002. Autocorrelation and linkage cause bias in evaluation of relational learners. In Inductive Logic Programming, Springer, 101–116.Google Scholar
Katzouris, N., Artikis, A. and Paliouras, G. 2015. Incremental learning of event definitions with inductive logic programming. Machine Learning 100, 2–3, 555–585.CrossRefGoogle Scholar
Kowalski, R. and Sergot, M. 1986. A logic-based calculus of events. New Generation Computing 4, 1, 67–95.CrossRefGoogle Scholar
Lopes, C. and Zaverucha, G. 2009. Htilde: scaling up relational decision trees for very large databases. In Proceedings of the 2009 ACM symposium on Applied Computing. ACM, 1475–1479.CrossRefGoogle Scholar
Muggleton, S. 1995. Inverse entailment and Progol. New generation computing 13, 3–4, 245–286.CrossRefGoogle Scholar
Ray, O. 2009. Nonmonotonic abductive inductive learning. Journal of Applied Logic 7, 3, 329–340.CrossRefGoogle Scholar
Richards, B. L. and Mooney, R. J. 1995. Automated refinement of first-order horn-clause domain theories. Machine Learning 19, 2, 95–131.CrossRefGoogle Scholar
Skarlatidis, A., Paliouras, G., Artikis, A. and Vouros, G. A. 2015. Probabilistic event calculus for event recognition. ACM Transactions on Computational Logic (TOCL) 16, 2, 11.CrossRefGoogle Scholar
Yang, H. and Fong, S. 2011. Moderated vfdt in stream mining using adaptive tie threshold and incremental pruning. In Data Warehousing and Knowledge Discovery, Springer, 471–483.CrossRefGoogle Scholar