Advertisement

Probabilistic Event Calculus Based on Markov Logic Networks

  • Anastasios Skarlatidis
  • Georgios Paliouras
  • George A. Vouros
  • Alexander Artikis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7018)

Abstract

In this paper, we address the issue of uncertainty in event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset of video surveillance.

Keywords

Soft Constraint Event Recognition Symbolic Method Markov Logic Network Event Calculus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefzbMATHGoogle Scholar
  2. 2.
    Artikis, A., Paliouras, G., Portet, F., Skarlatidis, A.: Logic-based representation, reasoning and machine learning for event recognition. In: DEBS, pp. 282–293 (2010c)Google Scholar
  3. 3.
    Artikis, A., Sergot, M., Paliouras, G.: A logic programming approach to activity recognition. In: ACM Workshop on Events in Multimedia (2010b)Google Scholar
  4. 4.
    Artikis, A., Skarlatidis, A., Paliouras, G.: Behaviour recognition from video content: a logic programming approach. IJAIT 19(2), 193–209 (2010a)Google Scholar
  5. 5.
    Biswas, R., Thrun, S., Fujimura, K.: Recognizing activities with multiple cues. In: Elgammal, A.M., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 255–270. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Brand, M., Oliver, N., Pentland, A.: Coupled hidden markov models for complex action recognition. In: CVPR, pp. 994–999. IEEE Computer Society (1997)Google Scholar
  7. 7.
    Doherty, P., Lukaszewicz, W., Szalas, A.: Computing circumscription revisited: A reduction algorithm. J. Autom. Reasoning 18(3), 297–336 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool Publishers (2009)Google Scholar
  9. 9.
    Dousson, C., Maigat, P.L.: Chronicle recognition improvement using temporal focusing and hierarchization. In: Veloso, M.M. (ed.) IJCAI, pp. 324–329 (2007)Google Scholar
  10. 10.
    Helaoui, R., Niepert, M., Stuckenschmidt, H.: Recognizing interleaved and concurrent activities: A statistical-relational approach. In: PerCom, pp. 1–9. IEEE (2011)Google Scholar
  11. 11.
    Kembhavi, A., Yeh, T., Davis, L.S.: Why did the person cross the road (there)? scene understanding using probabilistic logic models and common sense reasoning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 693–706. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Generation Computing 4, 67–95 (1986)CrossRefGoogle Scholar
  13. 13.
    McCarthy, J.: Circumscription - a form of non-monotonic reasoning. Artificial Intelligence 13, 27–39 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Miller, R., Shanahan, M.: Some alternative formulations of the event calculus. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 452–490. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Morariu, V.I., Davis, L.S.: Multi-agent event recognition in structured scenarios. In: Computer Vision and Pattern Recognition (CVPR)Google Scholar
  16. 16.
    Mueller, E.T.: Event calculus. In: Handbook of Knowledge Representation, FAI, vol. 3, pp. 671–708 (2008)Google Scholar
  17. 17.
    Nonnengart, A., Weidenbach, C.: Computing small clause normal forms. In: Handbook of Automated Reasoning, vol. 1, pp. 335–367 (2001)Google Scholar
  18. 18.
    de Salvo Braz, R., Amir, E., Roth, D.: A survey of first-order probabilistic models. In: Innovations in Bayesian Networks. SCI, pp. 289–317 (2008)Google Scholar
  19. 19.
    Shanahan, M.: Solving the frame problem: a mathematical investigation of the common sense law of inertia. MIT Press, Cambridge (1997)Google Scholar
  20. 20.
    Shanahan, M.: The event calculus explained. In: Artificial Intelligence Today: Recent Trends and Developments, pp. 409–430 (1999)Google Scholar
  21. 21.
    Shet, V.D., Neumann, J., Ramesh, V., Davis, L.S.: Bilattice-based logical reasoning for human detection. In: CVPR (2007)Google Scholar
  22. 22.
    Shi, Y., Bobick, A.F., Essa, I.A.: Learning temporal sequence model from partially labeled data. In: CVPR (2), pp. 1631–1638. IEEE Computer Society (2006)Google Scholar
  23. 23.
    Tran, S.D., Davis, L.S.: Event modeling and recognition using markov logic networks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 610–623. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anastasios Skarlatidis
    • 1
    • 2
  • Georgios Paliouras
    • 1
  • George A. Vouros
    • 2
  • Alexander Artikis
    • 1
  1. 1.Institute of Informatics and Telecommunications, NCSR “Demokritos”AthensGreece
  2. 2.Department of Information and Communication Systems EngineeringUniversity of the AegeanSamosGreece

Personalised recommendations

Cite paper

Buy options