Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models

  • Krishna Dubba
  • Mehul Bhatt
  • Frank Dylla
  • David C. Hogg
  • Anthony G. Cohn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)


We propose an interleaved inductive-abductive model for reasoning about complex spatio-temporal narratives. Typed Inductive Logic Programming (Typed-ILP) is used as a basis for learning the domain theory by generalising from observation data, whereas abductive reasoning is used for noisy data correction by scenario and narrative completion thereby improving the inductive learning to get semantically meaningful event models. We apply the model to an airport domain consisting of video data for 15 turn-arounds from six cameras simultaneously monitoring logistical processes concerned with aircraft arrival, docking, departure etc and a verbs data set with 20 verbs enacted out in around 2500 vignettes. Our evaluation and demonstration focusses on the synergy afforded by the inductive-abductive cycle, whereas our proposed model provides a blue-print for interfacing common-sense reasoning about space, events and dynamic spatio-temporal phenomena with quantitative techniques in activity recognition.


Logic Programming Inductive Logic Programming Situation Calculus Bottom Clause Commonsense Reasoning 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bhatt, M.: Reasoning about space, actions and change: A paradigm for applications of spatial reasoning. In: Qualitative Spatial Representation and Reasoning: Trends and Future Directions. IGI Global, USA (2010)Google Scholar
  2. 2.
    Borg, M., Thirde, D., Ferryman, J., Fusier, F., Valentin, V., Brémond, F., Thonnat, M.: Video surveillance for aircraft activity monitoring. In: IEEE Conf. on Advanced Video and Signal Based Surveillance, AVSS 2005, pp. 16–21. IEEE (2006)Google Scholar
  3. 3.
    Needham, C.J., Santos, P.E., Magee, D.R., Devin, V., Hogg, D.C., Cohn, A.G.: Protocols from perceptual observations. Artificial Intelligence 167(1-2), 103–136 (2005)CrossRefGoogle Scholar
  4. 4.
    Dubba, K.S.R., Cohn, A.G., Hogg, D.C.: Event model learning from complex videos using ILP. In: ECAI, pp. 93–98 (2010)Google Scholar
  5. 5.
    Menon, V., Jayaraman, B., Govindaraju, V.: Integrating recognition and reasoning in smart environments. In: IET International Conf. on Intelligent Environments, pp. 1–8 (July 2008)Google Scholar
  6. 6.
    Mooney, R.J.: Integrating abduction and induction in machine learning. In: Flach, P.A., Kakas, A.C. (eds.) Abduction and Induction, pp. 181–191. Kluwer Academic Publishers (2000)Google Scholar
  7. 7.
    Hazarika, S.M., Cohn, A.G.: Abducing qualitative spatio-temporal histories from partial observations. In: Int. Conf. on Principles of Know. Representation and Reasoning (2002)Google Scholar
  8. 8.
    Bhatt, M., Flanagan, G.: Spatio-temporal abduction for scenario and narrative completion. In: Proceedings of the International Workshop on Spatio-Temporal Dynamics, ECAI 2010, pp. 31–36 (2010)Google Scholar
  9. 9.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gen. Comput. 4(1), 67–95 (1986)CrossRefGoogle Scholar
  10. 10.
    Moyle, S.: Using Theory Completion to Learn a Robot Navigation Control Program. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 182–197. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Ray, O.: Nonmonotonic abductive inductive learning. Journal of Applied Logic 7, 329–340 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Kakas, A.C., Riguzzi, F.: Abductive concept learning. New Generation Computing 18(3), 243–294 (2000)CrossRefGoogle Scholar
  13. 13.
    Blockeel, H., De Raedt, L., Jacobs, N., Demoen, B.: Scaling up ILP by learning from interpretations. Data Mining and Knowledge Discovery 3(1), 59–93 (1999)CrossRefGoogle Scholar
  14. 14.
    Cohn, A.G., Renz, J.: Qualitative spatial reasoning. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation. Elsevier (2007)Google Scholar
  15. 15.
    Bhatt, M., Guesgen, H., Woelfl, S., Hazarika, S.: Qualitative spatial and temporal reasoning: Emerging applications, trends, and directions. Spatial Cognition & Computation 11(1), 1–14 (2011)CrossRefGoogle Scholar
  16. 16.
    Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Int. Conf. on Principles of Know. Representation and Reasoning (1992)Google Scholar
  17. 17.
    Bhatt, M., Loke, S.: Modelling dynamic spatial systems in the situation calculus. Spatial Cognition and Computation 8(1), 86–130 (2008)CrossRefGoogle Scholar
  18. 18.
    Muggleton, S.: Inverse entailment and Progol. New Gen. Comp. 13(3&4), 245–286 (1995)CrossRefGoogle Scholar
  19. 19.
    Poole, D., Goebel, R., Aleliunas, R.: Theorist: A logical reasoning system for defaults and diagnosis. In: The Knowledge Frontier, pp. 331–352. Springer (1987)Google Scholar
  20. 20.
    Miller, R., Shanahan, M.: Narratives in the situation calculus. J. Log. Comput. 4(5), 513–530 (1994)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. Journal of Logic and Computation 2(6), 719 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26, 832–843 (1983)zbMATHCrossRefGoogle Scholar
  23. 23.
    Christiansen, H., Dahl, V.: HYPROLOG: A New Logic Programming Language with Assumptions and Abduction. In: Gabbrielli, M., Gupta, G. (eds.) ICLP 2005. LNCS, vol. 3668, pp. 159–173. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Van de Weghe, N., Cohn, A.G., De Tre, G., De Maeyer, P.: A qualitative trajectory calculus as a basis for representing moving objects in geographical information systems. Control and Cybernetics 35(1), 97 (2006)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krishna Dubba
    • 1
  • Mehul Bhatt
    • 2
  • Frank Dylla
    • 2
  • David C. Hogg
    • 1
  • Anthony G. Cohn
    • 1
  1. 1.School of ComputingUniversity of LeedsUK
  2. 2.SFB/TR 8 Spatial CognitionUniversity of BremenGermany

Personalised recommendations