skip to main content
10.1145/1827418.1827471acmconferencesArticle/Chapter ViewAbstractPublication PagesdebsConference Proceedingsconference-collections
tutorial

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

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.
[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.
[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.
[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.
[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.
[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.
[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.
[15]
L. Getoor and B. Taskar. Introduction to statistical relational learning. The MIT Press, 2007.
[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.
[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.
[19]
K. Kersting, L. De Raedt, and T. Raiko. Logical hidden markov models. Journal of Artificial Intelligence Research, 25(1):425--456, 2006.
[20]
R. Kowalski and M. Sergot. A logic-based calculus of events. New Generation Computing, 4(1):67--96, 1986.
[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.
[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.
[25]
S. Muggleton and C. Bryant. Theory completion using inverse entailment. In ILP, volume LNCS 1866, pages 130--146. Springer, 2000.
[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.
[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.
[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.
[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.
[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.
[37]
M. Thonnat. Semantic activity recognition. In Proceedings of ECAI, pages 3--7, 2008.
[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.

Cited By

View all

Index Terms

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

      Recommendations

      Comments

      Information & Contributors

      Information

      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
      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]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 July 2010

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tag

      1. event recognition

      Qualifiers

      • Tutorial

      Funding Sources

      Conference

      DEBS '10

      Acceptance Rates

      Overall Acceptance Rate 145 of 583 submissions, 25%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)11
      • Downloads (Last 6 weeks)5
      Reflects downloads up to 22 Dec 2024

      Other Metrics

      Citations

      Cited By

      View all

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media