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Logic-based representation, reasoning and machine learning for event recognition

Authors:

Alexander Artikis,

Georgios Paliouras,

François Portet,

Anastasios SkarlatidisAuthors Info & Claims

DEBS '10: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

Pages 282 - 293

https://doi.org/10.1145/1827418.1827471

Published: 12 July 2010 Publication History

Abstract

Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. This requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field.

References

[1]

A. Artikis, A. Skarlatidis, G. Paliouras, P. Karampiperis, and C. Spyropoulos. First knowledge of event definitions and reasoning algorithms for event recognition. In Deliverable 4.1.1 of the EU-funded FP7 PRONTO project (FP7-ICT 231738), 2010.

[2]

R. Biswas, S. Thrun, and K. Fujimura. Recognizing activities with multiple cues. In Workshop on Human Motion, LNCS 4814, pages 255--270. Springer, 2007.

Digital Library

[3]

L. Callens, G. Carrault, M.-O. Cordier, É. Fromont, F. Portet, and R. Quiniou. Intelligent adaptive monitoring for cardiac surveillance. In ECAI, pages 653--657, 2008.

Digital Library

[4]

G. Carrault, M. Cordier, R. Quiniou, and F. Wang. Temporal abstraction and inductive logic programming for arrhyhtmia recognition from electrocardiograms. Artificial Intelligence in Medicine, 28:231--263, 2003.

Digital Library

[5]

L. Chittaro and M. Dojat. Using a general theory of time and change in patient monitoring: Experiment and evaluation. Computers in Biology and Medicine, 27(5):435--452, 1997.

[6]

L. Chittaro and A. Montanari. Efficient temporal reasoning in the cached event calculus. Computational Intelligence, 12(3):359--382, 1996.

[7]

C. Choppy, O. Bertrand, and P. Carle. Coloured petri nets for chronicle recognition. In Proceedings of Conference on Reliable Software Technologies, volume LNCS 5570, pages 266--281. Springer, 2009.

Digital Library

[8]

L. De Raedt and K. Kersting. Probabilistic inductive logic programming. Probabilistic Inductive Logic Programming, pages 1--27, 2008.

[9]

R. de Salvo Braz, E. Amir, and D. Roth. A survey of first-order probabilistic models. In D. E. Holmes and L. C. Jain, editors, Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence, pages 289--317. Springer, 2008.

[10]

P. Domingos and D. Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool Publishers, 2009.

Digital Library

[11]

C. Dousson. Alarm driven supervision for télécommunication network II --- on-line chronicle recognition. Annales des Telecommunication, 51(9--10):501--508, 1996.

[12]

C. Dousson and P. L. Maigat. Chronicle recognition improvement using temporal focusing and hierarchisation. In IJCAI, pages 324--329, 2007.

Digital Library

[13]

A. Farrell, M. Sergot, M. Sallé, and C. Bartolini. Using the event calculus for tracking the normative state of contracts. International Journal of Cooperative Information Systems, 4(2--3):99--129, 2005.

[14]

F. Fessant, F. Clérot, and C. Dousson. Mining of an alarm log to improve the discovery of frequent patterns. In Industrial Conference on Data Mining, pages 144--152, 2004.

Digital Library

[15]

L. Getoor and B. Taskar. Introduction to statistical relational learning. The MIT Press, 2007.

Digital Library

[16]

M. Ghallab. On chronicles: Representation, on-line recognition and learning. In Principles of Knowledge Representation and Reasoning, pages 597--606, 1996.

[17]

A. Hakeem and M. Shah. Learning, detection and representation of multi-agent events in videos. Artificial Intelligence, 171(8--9):586--605, 2007.

Digital Library

[18]

S. Hongeng and R. Nevatia. Large-scale event detection using semi-hidden markov models. In Proceedings of Conference on Computer Vision, pages 1455--1462. IEEE, 2003.

Digital Library

[19]

K. Kersting, L. De Raedt, and T. Raiko. Logical hidden markov models. Journal of Artificial Intelligence Research, 25(1):425--456, 2006.

Digital Library

[20]

R. Kowalski and M. Sergot. A logic-based calculus of events. New Generation Computing, 4(1):67--96, 1986.

Digital Library

[21]

W. V. Laer. From Propositional to First Order Logic in Machine Learning and Data Mining. PhD thesis, K. U. Leuven, 2002.

[22]

D. Luckham. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, 2002.

Digital Library

[23]

R. Miller and M. Shanahan. The event calculus in a classical logic --- alternative axiomatizations. JETAI, 4(16), 2000.

[24]

E. Mueller. Commonsense Reasoning. Morgan Kaufmann, 2006.

Digital Library

[25]

S. Muggleton and C. Bryant. Theory completion using inverse entailment. In ILP, volume LNCS 1866, pages 130--146. Springer, 2000.

Digital Library

[26]

S. Muggleton and L. D. Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:629--679, 1994.

[27]

K. Murphy. Dynamic bayesian networks: representation, inference and learning. PhD thesis, University of California, Berkeley, 2002.

Digital Library

[28]

N. Nguyen, D. Phung, S. Venkatesh, and H. Bui. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In Proceedings of Conference on Computer Vision and Pattern Recognition, 2005.

Digital Library

[29]

A. Paschke. ECA-RuleML: An approach combining ECA rules with temporal interval-based KR event logics and transactional update logics. Technical Report 11, Technische Universität München, 2005.

[30]

A. Paschke and M. Bichler. Knowledge representation concepts for automated SLA management. Decision Support Systems, 46(1):187--205, 2008.

Digital Library

[31]

A. Paschke, A. Kozlenkov, and H. Boley. A homogeneous reaction rule language for complex event processing. In Proceedings of Workshop on Event driven Architecture, Processing and Systems, 2007.

[32]

L. Rabiner and B. Juang. A tutorial on hidden Markov models. Proceedings of the IEEE, 77(2):257--286, 1989.

[33]

O. Ray. Nonmonotonic abductive inductive learning. Journal of Applied Logic, 7(3), 2009.

[34]

M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62(1--2):107--136, 2006.

Digital Library

[35]

V. Shet, J. Neumann, V. Ramesh, and L. Davis. Bilattice-based logical reasoning for human detection. In Computer Vision and Pattern Recognition, pages 1--8. IEEE, 2007.

[36]

P. Singla and P. Domingos. Discriminative training of markov logic networks. In Proceedings of AAAI, pages 868--873. AAAI Press, 2005.

Digital Library

[37]

M. Thonnat. Semantic activity recognition. In Proceedings of ECAI, pages 3--7, 2008.

Digital Library

[38]

S. D. Tran and L. S. Davis. Event modeling and recognition using markov logic networks. In Proceedings of Computer Vision Conference, pages 610--623, 2008.

Digital Library

Cited By

Srinivasan ABain MBaskar A(2022)Learning explanations for biological feedback with delays using an event calculusMachine Language10.1007/s10994-021-06038-y111:7(2435-2487)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10994-021-06038-y
Chahuara PFleury APortet FVacher M(2016)On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic Smart Homes1Journal of Ambient Intelligence and Smart Environments10.3233/AIS-1603868:4(399-422)Online publication date: 21-Jul-2016
https://doi.org/10.3233/AIS-160386
Ivaschenko ANovikov AKosov DKuzmin V(2015)Moving sensors concept for distributed diagnostics2015 SAI Intelligent Systems Conference (IntelliSys)10.1109/IntelliSys.2015.7361274(1051-1053)Online publication date: Nov-2015
https://doi.org/10.1109/IntelliSys.2015.7361274
Show More Cited By

Index Terms

Logic-based representation, reasoning and machine learning for event recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Theory of computation
  1. Logic

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Published In

cover image ACM Conferences

DEBS '10: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems

July 2010

303 pages

ISBN:9781605589275

DOI:10.1145/1827418

Co-chair:
Peter Pietzuch
Imperial College London, UK
,
Conference Chair:
Jean Bacon
University of Cambridge, UK
,
Program Chairs:
Joe Sventek
University of Glasgow, UK
,
Ugur Cetintemel
Brown University, Rhode Island

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2010

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event recognition

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Tutorial

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Seventh Framework Programme

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DEBS '10

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DEBS '10: The 4th ACM International Conference on Distributed Event-based Systems

July 12 - 15, 2010

Cambridge, United Kingdom

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Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Srinivasan ABain MBaskar A(2022)Learning explanations for biological feedback with delays using an event calculusMachine Language10.1007/s10994-021-06038-y111:7(2435-2487)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10994-021-06038-y
Chahuara PFleury APortet FVacher M(2016)On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic Smart Homes1Journal of Ambient Intelligence and Smart Environments10.3233/AIS-1603868:4(399-422)Online publication date: 21-Jul-2016
https://doi.org/10.3233/AIS-160386
Ivaschenko ANovikov AKosov DKuzmin V(2015)Moving sensors concept for distributed diagnostics2015 SAI Intelligent Systems Conference (IntelliSys)10.1109/IntelliSys.2015.7361274(1051-1053)Online publication date: Nov-2015
https://doi.org/10.1109/IntelliSys.2015.7361274
Margara ACugola GTamburrelli GBellur UKothari R(2014)Learning from the pastProceedings of the 8th ACM International Conference on Distributed Event-Based Systems10.1145/2611286.2611289(47-58)Online publication date: 26-May-2014
https://dl.acm.org/doi/10.1145/2611286.2611289
Ivaschenko A(2014)Multi-agent Solution for Business Processes Management of 5PL Transportation ProviderS-BPM ONE - Scientific Research10.1007/978-3-319-06065-1_7(110-120)Online publication date: 2014
https://doi.org/10.1007/978-3-319-06065-1_7
Katzouris NArtikis AMakedon FKarkaletsis VPaliouras GMakedon FBetke MEl-Nasr MMaglogiannis I(2013)Event recognition for assisted independent livingProceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2504335.2504361(1-5)Online publication date: 29-May-2013
https://dl.acm.org/doi/10.1145/2504335.2504361
Gal AKeren SSondak MWeidlich MBlom HBockermann CChakravarthy SUrban SPietzuch PRundensteiner E(2013)Grand challengeProceedings of the 7th ACM international conference on Distributed event-based systems10.1145/2488222.2488282(319-324)Online publication date: 29-Jun-2013
https://dl.acm.org/doi/10.1145/2488222.2488282
Kaiser RWeiss W(2013)Virtual DirectorMedia Production, Delivery and Interaction for Platform Independent Systems10.1002/9781118706350.ch6(209-259)Online publication date: 6-Dec-2013
https://doi.org/10.1002/9781118706350.ch6
Artikis AEtzion OFeldman ZFournier FBry FPaschke AEugster PFetzer CBehrend A(2012)Event processing under uncertaintyProceedings of the 6th ACM International Conference on Distributed Event-Based Systems10.1145/2335484.2335488(32-43)Online publication date: 16-Jul-2012
https://dl.acm.org/doi/10.1145/2335484.2335488
Schaumeier JPitt JCabri G(2012)A Tripartite Analytic Framework for Characterising Awareness and Self-Awareness in Autonomic Systems ResearchProceedings of the 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Workshops10.1109/SASOW.2012.35(157-162)Online publication date: 10-Sep-2012
https://dl.acm.org/doi/10.1109/SASOW.2012.35
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