Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-07T19:28:08.442Z Has data issue: false hasContentIssue false
  • English
  • Français

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Published online by Cambridge University Press:  01 August 2021

NIKOS KATZOURIS Open the ORCID record for NIKOS KATZOURIS [Opens in a new window] ,
GEORGIOS PALIOURAS  and
ALEXANDER ARTIKIS
NIKOS KATZOURIS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece (e-mail: [email protected], [email protected])
GEORGIOS PALIOURAS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece (e-mail: [email protected], [email protected])
ALEXANDER ARTIKIS
Affiliation:
Institute of Informatics and Telecommunications, National Center for Scientific Research (NCSR) “Demokritos”, Athens, Greece Department of Maritime Studies, University of Piraeus, Piraeus, Greece (e-mail: [email protected])
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

Keywords

inductive logic programming and multi-relational data miningknowledge representation and nonmonotonic reasoning
Type
Rapid Communication
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 2 , March 2023 , pp. 362 - 386
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

This paper is an extended version of Katzouris and Artikis (2020), which has been nominated as a candidate for TPLP’s rapid publication track by KR2020’s program committee

References

Alevizos, E., Skarlatidis, A., Artikis, A. and Paliouras, G. 2017. Probabilistic complex event recognition: A survey. ACM Computing Surveys 50, 5, 71:1–71:31.Google Scholar
Artikis, A., Sergot, M. and Paliouras, G. 2015. An event calculus for event recognition. IEEE Transactions on Knowledge and Data Engineering, 27, 4, 895–908.CrossRefGoogle Scholar
Artikis, A., Skarlatidis, A., Portet, F. and Paliouras, G. 2012. Logic-based event recognition. Knowledge Engineering Review 27, 04, 469–506.Google 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
Bifet, A., Gavaldà, R., Holmes, G. and Pfahringer, B. 2018. Machine Learning for Data Streams: With Practical Examples in MOA. MIT Press.CrossRefGoogle Scholar
Cugola, G. and Margara, A. 2012. Processing flows of information: From data stream to complex event processing. ACM Computing Surveys (CSUR) 44, 3, 15.Google Scholar
De Raedt, L. 2008. Logical and Relational Learning. Springer Science & Business Media.Google Scholar
De Raedt, L., Kersting, K., Natarajan, S. and Poole, D. 2016. Statistical relational artificial intelligence: Logic, probability, and computation. Synthesis Lectures on Artificial Intelligence and Machine Learning 10, 2, 1–189.Google Scholar
Domingos, P. M. and Hulten, G. 2000. Mining high-speed data streams. In ACM SIGKDD, 71–80.Google Scholar
Duchi, J., Hazan, E. and Singer, Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, Jul, 2121–2159.Google Scholar
Guimarães, V., Paes, A. and Zaverucha, G. 2019. Online probabilistic theory revision from examples with proppr. Machine Learning 108, 7, 1165–1189.CrossRefGoogle Scholar
Huynh, T. N. and Mooney, R. J. 2009. Max-margin weight learning for markov logic networks. In ECML-2009. Springer, 564–579.Google Scholar
Huynh, T. N. and Mooney, R. J. 2011. Online max-margin weight learning for markov logic networks. In SDM. SIAM, 642–651.Google Scholar
Katzouris, N. 2017. Scalable relational learning for event recognition. PhD Thesis, University of Athens. http://users.iit.demokritos.gr/nkatz/papers/nkatz-phd.pdf Google Scholar
Katzouris, N. and Artikis, A. 2020. WOLED: A tool for online learning weighted answer set rules for temporal reasoning under uncertainty. In KR 2020.CrossRefGoogle 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.Google Scholar
Katzouris, N., Artikis, A. and Paliouras, G. 2016. Online learning of event definitions. TPLP 16, 5-6, 817–833.Google Scholar
Katzouris, N., Artikis, A. and Paliouras, G. 2019. Parallel online event calculus learning for complex event recognition. Future Generation Computer Systems 94, 468–478.CrossRefGoogle Scholar
Katzouris, N., Michelioudakis, E., Artikis, A. and Paliouras, G. 2018. Online learning of weighted relational rules for complex event recognition. In ECML-PKDD 2018. 396–413.CrossRefGoogle Scholar
Law, M., Russo, A. and Broda, K. 2015. The ILASP system for learning answer set programs. www.ilasp.com.CrossRefGoogle Scholar
Law, M., Russo, A. and Broda, K. 2018. Inductive learning of answer set programs from noisy examples. Advances in Cognitive Systems.Google Scholar
Lee, J., Talsania, S. and Wang, Y. 2017. Computing LPMLN using ASP and MLN solvers. Theory and Practice of Logic Programming 17, 5–6, 942–960.CrossRefGoogle Scholar
Lee, J. and Wang, Y. 2016. Weighted rules under the stable model semantics. In KR, 2016.Google Scholar
Lifschitz, V. 2019. Answer Set Programming. Springer.CrossRefGoogle Scholar
Michelioudakis, E., Skarlatidis, A., Paliouras, G. and Artikis, A. 2016. Osla: Online structure learning using background knowledge axiomatization. In ECML. Springer, 232–247.Google Scholar
Mueller, E. T. 2014. Commonsense Reasoning: An Event Calculus Based Approach. Morgan Kaufmann.Google Scholar
Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N. and Theodoridis, Y. 2017. Online event recognition from moving vessel trajectories. GeoInformatica 21, 2, 389–427.CrossRefGoogle Scholar
Ray, O. 2009. Nonmonotonic abductive inductive learning. Journal of Applied Logic 7, 3, 329–340.CrossRefGoogle Scholar
Skarlatidis, A. and Michelioudakis, E. 2014. Logical Markov Random Fields (LoMRF): An open-source implementation of Markov Logic Networks.Google 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.Google Scholar
Tsilionis, E., Koutroumanis, N., Nikitopoulos, P., Doulkeridis, C. and Artikis, A. 2019. Online event recognition from moving vehicles: Application paper. Theory Pract. Log. Program. 19, 5–6, 841–856.CrossRefGoogle Scholar