Behaviour recognition from video content: A logic programming approach

International Journal on Artificial Intelligence Tools

Volumn 19, Issue 2, 2010, Pages 193-209

  Artikis, Alexander ^a   Skarlatidis, Anastasios ^a,b   Paliouras, Georgios ^a  

^a INSTITUTE OF INFORMATICS AND TELECOMMUNICATIONS   (Greece)

^b UNIVERSITY OF THE AEGEAN   (Greece)

Author keywords

Event calculus; Event recognition; Machine learning

Indexed keywords

EVENT CALCULUS; EVENT RECOGNITION; MACHINE LEARNING TECHNIQUES; MACHINE-LEARNING; SHORT PERIODS; SURVEILLANCE VIDEO; SYMBOLIC REPRESENTATION; TEMPORAL CONSTRAINTS; VIDEO CONTENTS; VIDEO FRAME;

CALCULATIONS; LEARNING ALGORITHMS; LEARNING SYSTEMS; LOGIC PROGRAMMING; VISUAL COMMUNICATION;

SECURITY SYSTEMS;

EID: 77951574331     PISSN: 02182130     EISSN: None     Source Type: Journal    
DOI: 10.1142/S021821301000011X     Document Type: Conference Paper

References (28)

1. D. Alrajeh, O. Ray, A. Russo, and S. Uchitel. Extracting requirements from scenarios with ILP. In Inductive Logic Programming, volume LNAI 4455. Springer, 2007.
2. A. Artikis, M. Sergot, and J. Pitt. Specifying norm-governed computational societies. ACM Transactions on Computational Logic, 10 (1), 2009.
3. K. Clark. Negation as failure. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 293-322. Plenum Press, 1978.
4. C. Dousson. Extending and unifying chronicle representation with event counters. In Proceedings of European Conference on Artificial Intelligence (ECAI), pages 257-261. IOS Press, 2002.
5. C. Dousson and P. Le Maigat. Chronicle recognition improvement using temporal focusing and hierarchisation. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 324-329, 2007.
6. M. Ghallab. On chronicles: Representation, on-line recognition and learning. In Proceedings of Conference on Principles of Knowledge Representation and Reasoning, pages 597-606, 1996.
7. D. Kosmopoulos, P. Antonakaki, K. Valasoulis, A. Kesidis, and S. Perantonis. Human behaviour classification using multiple views. In Proceedings of Hellenic Conference on Artificial Intelligence, volume 5138. Springer, 2008.
8. R. Kowalski and M. Sergot. A logic-based calculus of events. New Generation Computing, 4 (1) :67-96, 1986.
9. R. Miller and M. Shanahan. The event calculus in a classical logic - alternative axiomatizations. Journal of Electronic Transactions on Artificial Intelligence, 4 (16), 2000.
10. S. Moyle. Using theory completion to learn a robot navigation control program. In ILP, volume 2583 of Lecture Notes in Computer Science, pages 182-197, 2002. (Pubitemid 36242060)
11. S. Moyle and S. Muggleton. Learning programs in the event calculus. In ILP, volume 1297 of Lecture Notes in Computer Science, pages 205-212. Springer, 1997. (Pubitemid 127124392)
12. S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13(3):245-286, 1995.
13. S. Muggleton and C. H. Bryant. Theory completion using inverse entailment. In ILP, volume 1866 of Lecture Notes in Computer Science, pages 130-146. Springer, 2000.
14. A. Paes, G. Zaverucha, and V. Santos Costa. Revising first-order logic theories from examples through stochastic local search. In Inductive Logic Programming, pages 200-210, 2007. (Pubitemid 351333634)
15. A. Paschke. ECA-RuleML: An approach combining ECA rules with temporal intervalbased KR event/action logics and transactional update logics. Technical Report 11, Technische Universität Mun̈chen, 2005.
16. A. Paschke and M. Bichler. Knowledge representation concepts for automated SLA management. Decision Support Systems, 46 (1) :187-205, 2008.
17. A. Paschke, A. Kozlenkov, and H. Boley. A homogeneous reaction rule language for complex event processing. In Proceedings of International Workshop on Event-driven Architecture, Processing and Systems, 2007.
18. O. Ray. Hybrid abductive inductive learning. PhD thesis, Department of Computing, Imperial College London, UK, 2005.
19. O. Ray. Nonmonotonic abductive inductive learning. Journal of Applied Logic, 7 (3) :329-340, 2009.
20. O. Ray, K. Broda, and A. Russo. Hybrid abductive inductive learning: A generalisation of Progol. In T. Horvath and A. Yamamoto, editors, International Conference on Inductive Logic Programming, pages 311-328. Springer, October 2003. (Pubitemid 37273941)
21. O. Ray, K. Broda, and A. Russo. Generalised kernel sets for inverse entailment. In ICLP, volume 3132 of Lecture Notes in Computer Science, pages 165-179. Springer, 2004. (Pubitemid 39224272)
22. O. Ray, K. Broda, and A. Russo. A hybrid abductive inductive proof procedure. Logic Journal of the IGPL, 12 (5) :371-397, 2004.
23. O. Ray and A. Kakas. Prologica: a practical system for abductive logic programming. In J. Dix and A. Hunter, editors, International Workshop on Non-monotonic Reasoning, pages 304-312, May 2006.
24. B. Richards and R. Mooney. Automated refinement of first-order horn-clause domain theories. Machine Learning, 19 (2) :95-131, 1995.
25. M. Shanahan. The event calculus explained. In M. Wooldridge and M. Veloso, editors, Artificial Intelligence Today, LNAI 1600, pages 409-430. Springer, 1999.
26. A. Srinivasan. The aleph manual. Available at: http://web.comlab.ox.ac. uk/activities/machinelearning/Aleph/.
27. M. Stickel. A prolog technology theorem prover: A new exposition and implementation in prolog. Theoretical Computer Science, 104 (1) :109-128, 1992.
28. S. Wrobel. First-order theory refinement. In L. De Raedt, editor, Advances in Inductive Logic Programming, chapter First order theory refinement, pages 14-33. IOS Press, 1996.