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Logic-based event recognition

Published online by Cambridge University Press:  12 November 2012

Alexander Artikis ,
Anastasios Skarlatidis ,
François Portet  and
Georgios Paliouras
Alexander Artikis
Affiliation:
Institute of Informatics and; Telecommunications, NCSR “Demokritos”, Athens 15310, Greece; e-mail: paliourg@iit.demokritos.gr, anskarl@iit.demokritos.gr
Anastasios Skarlatidis
Affiliation:
Institute of Informatics and; Telecommunications, NCSR “Demokritos”, Athens 15310, Greece; e-mail: paliourg@iit.demokritos.gr, anskarl@iit.demokritos.gr Department of Information and Communication Systems Engineering, University of the Aegean, Greece
François Portet
Affiliation:
Laboratoire d'Informatique de Grenoble, CNRS/UJF/INPG/UPMF UMR 5217, F-38041 Grenoble, France; e-mail: Francois.Portet@imag.fr
Georgios Paliouras
Affiliation:
Institute of Informatics and; Telecommunications, NCSR “Demokritos”, Athens 15310, Greece; e-mail: paliourg@iit.demokritos.gr, anskarl@iit.demokritos.gr
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Abstract

Today's organizations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by using event recognition systems that detect events/activities of special significance within an organization, given streams of ‘low-level’ information that is very difficult to be utilized by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the Transmission Control Protocol/Internet Protocol messages, the recognition of suspicious trader behaviour given the transactions in a financial market and the recognition of whale songs given a symbolic representation of whale sounds. Various 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 have 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. We illustrate the reviewed approaches with the use of a real-world case study: event recognition for city transport management.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Issue 4 , December 2012 , pp. 469 - 506
Copyright
Copyright © Cambridge University Press 2012

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References

Akman, V., Erdogan, S., Lee, J., Lifschitz, V., Turner, H. 2004. Representing the zoo world and the traffic world in the language of the Causal Calculator. Artificial Intelligence 153(1–2), 105–140.CrossRefGoogle Scholar
Allen, J. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843.CrossRefGoogle Scholar
Álvarez, M. R., Félix, P., Cariñena, P., Otero, A. 2010. A data mining algorithm for inducing temporal constraint networks. In Proceedings of the International Conference on Information Processing and Management of Uncertainty (IPMU), 300–309.Google Scholar
Arasu, A., Babu, S., Widom, J. 2006. The CQL continuous query language: semantic foundations and query execution. The VLDB Journal 15(2), 121–142.CrossRefGoogle Scholar
Artikis, A., Paliouras, G., Portet, F., Skarlatidis, A. 2010a. Logic-based representation, reasoning and machine learning for event recognition. In Proceedings of the Conference on Distributed Event-Based Systems (DEBS). ACM Press, 282–293.Google Scholar
Artikis, A., Sergot, M., Paliouras, G. 2010b. A logic programming approach to activity recognition. In Proceedings of the ACM Workshop on Events in Multimedia.CrossRefGoogle Scholar
Artikis, A., Kukurikos, A., Paliouras, G., Karampiperis, P., Spyropoulos, C. 2011. Final Version of Knowledge Base of Event Definitions and Reasoning Algorithms for Event Recognition. Deliverable 4.1.2 of EU-funded FP7 PRONTO project (FP7-ICT 231738). Available from the authors.Google Scholar
Biswas, R., Thrun, S., Fujimura, K. 2007. Recognizing activities with multiple cues. In Proceedings of the Workshop on Human Motion, Lecture Notes in Computer Science 4814, 255–270. Springer.CrossRefGoogle Scholar
Callens, L., Carrault, G., Cordier, M.-O., Fromont, É., Portet, F., Quiniou, R. 2008. Intelligent adaptive monitoring for cardiac surveillance. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 653–657.Google Scholar
Carrault, G., Cordier, M., Quiniou, R., Wang, F. 2003. Temporal abstraction and inductive logic programming for arrhyhtmia recognition from electrocardiograms. Artificial Intelligence in Medicine 28, 231–263.CrossRefGoogle Scholar
Cervesato, I., Franceschet, M., Montanari, A. 1997. Modal event calculi with preconditions. In Proceedings of the Workshop on Temporal Reasoning (TIME). IEEE Computer Society, 38–45.Google Scholar
Cervesato, I., Franceschet, M., Montanari, A. 1998. The complexity of model checking in modal event calculi with quantifiers. Journal of Electronic Transactions on Artificial Intelligence 2, 1–23. http://www.ida.liu.se/ext/etai.Google Scholar
Cervesato, I., Franceschet, M., Montanari, A. 2000. A guided tour through some extensions of the event calculus. Computational Intelligence 16, 307–347.CrossRefGoogle Scholar
Cervesato, I., Montanari, A. 2000. A calculus of macro-events: Progress report. In Proceedings of the 7th International Workshop on Temporal Representation and Reasoning (TIME), 47–58.Google Scholar
Chaudet, H. 2006. Extending the event calculus for tracking epidemic spread. Artificial Intelligence in Medicine 38(2), 137–156.CrossRefGoogle ScholarPubMed
Chesani, F., Mello, P., Montali, M., Torroni, P. 2009. Commitment tracking via the reactive event calculus. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 91–96.Google Scholar
Chittaro, L., Dojat, M. 1997. Using a general theory of time and change in patient monitoring: experiment and evaluation. Computers in Biology and Medicine 27(5), 435–452.CrossRefGoogle ScholarPubMed
Chittaro, L., Montanari, A. 1996. Efficient temporal reasoning in the cached event calculus. Computational Intelligence 12(3), 359–382.CrossRefGoogle Scholar
Choppy, C., Bertrand, O., Carle, P. 2009. Coloured petri nets for chronicle recognition. In Proceedings of the Ada-Europe International Conference on Reliable Software Technologies, Lecture Notes in Computer Science 5570, 266–281. Springer.CrossRefGoogle Scholar
Clark, K. 1978. Negation as failure. In Logic and Databases, Gallaire, H. & Minker, J. (eds). Plenum Press, 293–322.Google Scholar
Craven, R. 2006. Execution Mechanisms for the Action Language C+. PhD thesis, University of London.Google Scholar
Cugola, G., Margara, A. 2011. Processing flows of information: from data stream to complex event processing. ACM Computing Surveys 44(3).CrossRefGoogle Scholar
Damlen, P., Wakefield, J., Walker, S. 1999. Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61(2), 331–344.CrossRefGoogle Scholar
De Raedt, L., Kersting, K. 2008. Probabilistic inductive logic programming. Probabilistic Inductive Logic Programming: Theory and Applications 4911, 1–27.CrossRefGoogle Scholar
De Salvo Braz, R., Amir, E., Roth, D. 2008. A survey of first-order probabilistic models. In Innovations in Bayesian Networks, Holmes, D. E. & Jain, L. C. (eds). Studies in Computational Intelligence 156, 289–317. Springer.CrossRefGoogle Scholar
Dechter, R., Meiri, I., Pearl, J. 1991. Temporal constraint networks. Artificial Intelligence 49, 61–95.CrossRefGoogle Scholar
Dempster, A. P., Laird, N. M., Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39(1), 1–38.Google Scholar
Denecker, M., Kakas, A. 2000. Special issue: abductive logic programming. Journal of Logic Programming 44(1–3), 1–4.CrossRefGoogle Scholar
Denecker, M., Kakas, A. 2002. Abduction in logic programming. In Computational Logic: Logic Programming and Beyond, Kakas, A. and Sadri, F. (eds), Lecture Notes in Computer Science 2407, 99–134. Springer.CrossRefGoogle Scholar
Denecker, M., Belleghem, K., Duchatelet, G., Piessens, F., Schreye, D. 1996. A realistic experiment in knowledge representation in open event calculus: protocol specification. In Proceedings of the Joint International Conference and Symposium on Logic Programming (JICSLP), Maher, M. (ed.). MIT Press, 170–184.Google Scholar
Doherty, P., Gustafsson, J., Karlsson, L., Kvarnström, J. 1998. (TAL) temporal action logics: language specification and tutorial. Electronic Transactions on Artificial Intelligence 2(3–4), 273–306.Google Scholar
Domingos, P., Lowd, D. 2009. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan & Claypool Publishers.CrossRefGoogle Scholar
Dousson, C. 1996. Alarm driven supervision for télécommunication network II – on-line chronicle recognition. Annales des Telecommunication 51(9–10), 501–508.CrossRefGoogle Scholar
Dousson, C. 2002. Extending and unifying chronicle representation with event counters. In Proceedings of the European Conference on Artificial Intelligence (ECAI). IOS Press, 257–261.Google Scholar
Dousson, C., Duong, T. V. 1999. Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 620–626.Google Scholar
Dousson, C., Maigat, P. L. 2006. Improvement of chronicle-based monitoring using temporal focalization and hierarchisation. In Proceedings of the International Workshop on Principles of Diagnosis (DX), 257–261.Google Scholar
Dousson, C., Maigat, P. L. 2007. Chronicle recognition improvement using temporal focusing and hierarchisation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 324–329.Google Scholar
Dousson, C., Gaborit, P., Ghallab, M. 1993. Situation recognition: representation and algorithms. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 166–174.Google Scholar
Dousson, C., Pentikousis, K., Sutinen, T., Mäkelä, J. 2007. Chronicle recognition for mobility management triggers. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC), 305–310.Google Scholar
Dzeroski, S., Lavrac, N. (eds) 2001. Relational Data Mining. Springer.CrossRefGoogle Scholar
Etzion, O., Niblett, P. 2010. Event Processing in Action. Manning Publications Co.Google Scholar
Farrell, A., Sergot, M., Sallé, M., Bartolini, C. 2005. Using the event calculus for tracking the normative state of contracts. International Journal of Cooperative Information Systems 4(2–3), 99–129.CrossRefGoogle Scholar
Fessant, F., Clérot, F., Dousson, C. 2004. Mining of an alarm log to improve the discovery of frequent patterns. In Proceedings of the Industrial Conference on Data Mining, 144–152.Google Scholar
Gao, F., Sripada, Y., Hunter, J., Portet, F. 2009. Using temporal constraints to integrate signal analysis and domain knowledge in medical event detection. In Artificial Intelligence in Medicine, Combi, C., Shahar, Y. & Abu-Hanna, A. (eds). Lecture Notes in Computer Science 5651,46–55. Springer.CrossRefGoogle Scholar
Getoor, L., Taskar, B. 2007. Introduction to Statistical Relational Learning. The MIT Press.CrossRefGoogle Scholar
Ghallab, M. 1996. On chronicles: representation, on-line recognition and learning. In Proceedings of the Conference on Principles of Knowledge Representation and Reasoning, 597–606.Google Scholar
Ghallab, M., Alaoui, A. M. 1989. Managing efficiently temporal relations through indexed spanning trees. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1297–1303.Google Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H. 2004. Nonmonotonic causal theories. Artificial Intelligence 153(1–2), 49–104.CrossRefGoogle Scholar
Hakeem, A., Shah, M. 2007. Learning, detection and representation of multi-agent events in videos. Artificial Intelligence 171(8–9), 586–605.CrossRefGoogle Scholar
Helaoui, R., Niepert, M., Stuckenschmidt, H. 2010. A statistical-relational activity recognition framework for ambient assisted living systems. In ISAmI, Augusto, J. C., Corchado, J. M., Novais, P. & Analide, C. (eds). Advances in Soft Computing 72, 247–254. Springer.Google Scholar
Hirate, Y., Yamana, H. 2006. Sequential pattern mining with time intervals. In Advances in Knowledge Discovery and Data Mining, Ng, W.-K., Kitsuregawa, M., Li, J. & Chang, K. (eds). Springer, 775–779.CrossRefGoogle Scholar
Hongeng, S., Nevatia, R. 2003. Large-scale event detection using semi-hidden markov models. In Proceedings of the Conference on Computer Vision. IEEE, 1455–1462.Google Scholar
Huynh, T., Mooney, R. 2008. Discriminative structure and parameter learning for Markov logic networks. In Proceedings of the 25th International Conference on Machine learning. ACM, 416–423.Google Scholar
Kakas, A. C., Kowalski, R. A., Toni, F. 1992. Abductive logic programming. Journal of Logic and Computation 2(6), 719–770.CrossRefGoogle Scholar
Kembhavi, A., Yeh, T., Davis, L. S. 2010. Why did the person cross the road (there)? scene understanding using probabilistic logic models and common sense reasoning. In ECCV (2), Daniilidis, K., Maragos, P. & Paragios, N. (eds). Lecture Notes in Computer Science 6312, 693–706. Springer.CrossRefGoogle Scholar
Kersting, K., De Raedt, L., Raiko, T. 2006. Logical hidden Markov models. Journal of Artificial Intelligence Research 25(1), 425–456.CrossRefGoogle Scholar
Kohonen, T. 2001. Self-Organising Maps, 3rd ed. Springer.CrossRefGoogle Scholar
Kok, S., Domingos, P. 2005. Learning the structure of Markov logic networks. In Proceedings of the 22nd international conference on Machine learning. ACM, 441–448.Google Scholar
Kok, S., Domingos, P. 2009. Learning Markov logic network structure via hypergraph lifting. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 505–512.Google Scholar
Kok, S., Domingos, P. 2010. Learning Markov logic networks using structural motifs. In Proceedings of the International Conference on Machine Learning (ICML), Fürnkranz, J. & Joachims, T. (eds). Omnipress, 551–558.Google Scholar
Konstantopoulos, S., Camacho, R., Fonseca, N., Costa, V. S. 2008. Induction as a search. In Artificial Intelligence for Advanced Problem Solving Techniques, Vrakas, D. & Vlahavas, I. (eds). IGI Global, Ch. VII, 158–205.Google Scholar
Kowalski, R., Sadri, F. 1997. Reconciling the event calculus with the situation calculus. Journal of Logic Programming 31, 39–58.CrossRefGoogle Scholar
Kowalski, R., Sergot, M. 1986. A logic-based calculus of events. New Generation Computing 4(1), 67–96.CrossRefGoogle Scholar
Kvarnström, J. 2005. TALplanner and Other Extensions to Temporal Action Logic. PhD thesis, Department of Computer and Information Science, Linköping University.Google Scholar
Laer, W. V. 2002. From Propositional to First Order Logic in Machine Learning and Data Mining. PhD thesis, K. U. Leuven.Google Scholar
Le Guillou, X., Cordier, M.-O., Robin, S., Rozé, L. 2008. Chronicles for on-line diagnosis of distributed systems. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 194–198.Google Scholar
Lowd, D., Domingos, P. 2007. Efficient weight learning for Markov logic networks. In Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 200–211.Google Scholar
Luckham, D. 2002. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley.Google Scholar
Luckham, D., Schulte, R. 2008. Event Processing Glossary – Version 1.1. Event Processing Technical Society. http://www.ep-ts.com/ .Google Scholar
Lv, F., Nevatia, R., Lee, M. 2005. 3D human action recognition using spatio-temporal motion templates. In Proceedings of the International Workshop on Computer Vision in Human-Computer Interaction (ICCV), 120–130.Google Scholar
Mackworth, A., Freuder, E. 1985. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence 25, 65–74.CrossRefGoogle Scholar
Mannila, H., Toivonen, H., Verkamo, A.I. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289.CrossRefGoogle Scholar
McCarthy, J., Hayes, P. 1969. Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence 4, 463–502.Google Scholar
Mihalkova, L., Mooney, R. 2007. Bottom-up learning of Markov logic network structure, In Proceedings of the International Conference on Machine learning (ICML). ACM, 625–632.Google Scholar
Miller, R., Shanahan, M. 1999. The event calculus in a classical logic – alternative axiomatizations. Journal of Electronic Transactions on Artificial Intelligence 3(A), 77–105.Google Scholar
Miller, R., Shanahan, M. 2002. Some alternative formulations of the event calculus. In Computational Logic: Logic Programming and Beyond – Essays in Honour of Robert A. Kowalski, Lecture Notes in Arificial Intelligence 2408, 452–490. Springer.CrossRefGoogle Scholar
Morin, B., Debar, H. 2003. Correlation of intrusion symptoms: an application of chronicles. In Proceedings of the 6th International Conference on Recent Advances in Intrusion Detection (RAID'03), Pittsburgh, USA.CrossRefGoogle Scholar
Moyle, S. 2002. Using theory completion to learn a robot navigation control program. In Inductive Logic Programming, Lecture Notes in Computer Science 2583, 182–197. Springer.CrossRefGoogle Scholar
Mueller, E. 2006a. Commonsense Reasoning. Morgan Kaufmann.CrossRefGoogle Scholar
Mueller, E. 2006b. Event calculus and temporal action logics compared. Artificial Intelligence 170(11), 1017–1029.CrossRefGoogle Scholar
Muggleton, S. 1991. Inductive logic programming. New Generation Computing 8(4), 295–318.CrossRefGoogle Scholar
Muggleton, S. 1995. Inverse entailment and Progol. New Generation Computing 13(3–4), 245–286.CrossRefGoogle Scholar
Muggleton, S., Bryant, C. 2000. Theory completion using inverse entailment. In Inductive Logic Programming, Lecture Notes in Computer Science 1866, 130–146. Springer.CrossRefGoogle Scholar
Muggleton, S., Raedt, L. D. 1994. Inductive logic programming: theory and methods. Journal of Logic Programming 19/20, 629–679.CrossRefGoogle Scholar
Murphy, K. 2002. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley.Google Scholar
Nebel, B., Bürckert, H.-J. 1995. Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra. Journal of the ACM 42(1), 43–66.CrossRefGoogle Scholar
Nédellec, C., Rouveirol, C., Adé, H., Bergadano, F., Tausend, B. 1996. Declarative bias in ILP. In Advances in Inductive Logic Programming, Raedt, L. D. (ed.). IOS Press, 82–103.Google Scholar
Nguyen, N., Phung, D., Venkatesh, S., Bui, H. 2005. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In Proceedings of the Conference on Computer Vision and Pattern Recognition.Google Scholar
Paschke, A. 2005. ECA-RuleML: An Approach Combining ECA Rules with Temporal Interval-based KR Event/Action Logics and Transactional Update Logics. Technical report 11, Technische Universität München.Google Scholar
Paschke, A. 2006. ECA-LP/ECA-RuleML: A Homogeneous Event-Condition-Action Logic Programming Language. Technical report, CoRR abs/cs/0609143.Google Scholar
Paschke, A., Bichler, M. 2008. Knowledge representation concepts for automated SLA management. Decision Support Systems 46(1), 187–205.CrossRefGoogle Scholar
Paschke, A., Kozlenkov, A. 2009. Rule-based event processing and reaction rules. In Proceedings of the RuleML, Lecture Notes in Computer Science 5858, 53–66. Springer.CrossRefGoogle Scholar
Paschke, A., Kozlenkov, A., Boley, H. 2007. A homogeneous reaction rule language for complex event processing. In Proceedings of the International Workshop on Event-driven Architecture, Processing and Systems.Google Scholar
Poon, H., Domingos, P. 2006. Sound and efficient inference with probabilistic and deterministic dependencies. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference. AAAI Press.Google Scholar
Poon, H., Domingos, P. 2008. Joint unsupervised coreference resolution with Markov Logic. In Proceedings of the the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 650–659.Google Scholar
Pottebaum, J., Marterer, R. 2010. Final Requirements, Use Case and Scenario Specification. Deliverable 6.1.2 of the EU-funded FP7 PRONTO project (FP7-ICT 231738). Available from the authors.Google Scholar
Quinlan, J. R., Cameron-Jones, R. M. 1995. Induction of logic programs: foil and related systems. New Generation Computing 13, 287–312.CrossRefGoogle Scholar
Rabiner, L., Juang, B. 1989. A tutorial on hidden Markov models. Proceedings of the IEEE 77(2), 257–286.CrossRefGoogle Scholar
Ray, O. 2009. Nonmonotonic abductive inductive learning. Journal of Applied Logic 7(3), 329–340.CrossRefGoogle Scholar
Reiter, R. 2001. Knowledge in Action: Logical Foundations for Describing and Implementing Dynamical Systems. The MIT Press.CrossRefGoogle Scholar
Richardson, M., Domingos, P. 2006. Markov logic networks. Machine Learning 62(1–2), 107–136.CrossRefGoogle Scholar
Sadri, F., Kowalski, R. 1995. Variants of the event calculus. In Proceedings of the International Conference on Logic Programming. The MIT Press, 67–81.Google Scholar
Shanahan, M. 1999. The event calculus explained. In Artificial Intelligence Today, Wooldridge, M. & Veloso, M. (eds). Lecture Notes in Artificial Intelligence 1600, 409–430. Springer.CrossRefGoogle Scholar
Shet, V., Harwood, D., Davis, L. 2005. VidMAP: video monitoring of activity with Prolog. In Proceedings of the International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 224–229.Google Scholar
Shet, V., Harwood, D., Davis, L. 2006. Multivalued default logic for identity maintenance in visual surveillance. In Proceedings of the European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science 3954, 119–132. Springer.CrossRefGoogle Scholar
Shet, V., Neumann, J., Ramesh, V., Davis, L. 2007. Bilattice-based logical reasoning for human detection. In Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1–8.Google Scholar
Singla, P., Domingos, P. 2005. Discriminative training of Markov logic networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Veloso, M. M. & Kambhampati, S. (eds). 868–873.Google Scholar
Singla, P., Domingos, P. 2006. Memory-efficient inference in relational domains. In Proceedings of the AAAI Conference on Artificial Intelligence.Google Scholar
Singla, P., Domingos, P. 2008. Lifted first-order belief propagation. In Proceedings of the AAAI Conference on Artificial Intelligence, Fox D. & Gomes C. P. (eds), 1094–1099.Google Scholar
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A. C., Muggleton, S. 2006. Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64(1–3), 209–230.CrossRefGoogle Scholar
Teymourian, K., Paschke, A. 2009. Semantic rule-based complex event processing. In Proceedings of the RuleML, Lecture Notes in Computer Science 5858, 82–92. Springer.CrossRefGoogle Scholar
Thielscher, M. 1999. From situation calculus to fluent calculus: state update axioms as a solution to the inferential frame problem. Artificial Intelligence 111(1–2), 277–299.CrossRefGoogle Scholar
Thielscher, M. 2001. The qualification problem: a solution to the problem of anomalous models. Artificial Intelligence 131(1–2), 1–37.CrossRefGoogle Scholar
Thonnat, M. 2008. Semantic activity recognition. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 3–7.Google Scholar
Tran, S. D., Davis, L. S. 2008. Event modeling and recognition using markov logic networks. In Proceedings of Computer Vision Conference, 610–623.Google Scholar
Vautier, A., Cordier, M.-O., Quiniou, R. 2007. Towards data mining without information on knowledge structure. In Knowledge Discovery in Databases, Kok, J., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenic, D. & Skowron, A. (eds). 300–311.Google Scholar
Vilain, M. B., Kautz, H. A. 1986. Constraint propagation algorithms for temporal reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, 377–382.Google Scholar
Vu, V.-T., Brémond, F., Thonnat, M. 2003. Automatic video interpretation: a novel algorithm for temporal scenario recognition. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 1295–1302.Google Scholar
Wellner, B. R. 1999. An Abductive–Inductive Learning Framework for Logic-Based Agents. MS thesis, Imperial College of Science Technology and Medicine.Google Scholar
Wu, C., Aghajan, H. K. 2010. Recognizing objects in smart homes based on human interaction. In ACIVS (2), Blanc-Talon, J., Bone, D., Philips, W., Popescu, D. C. & Scheunders, P., (eds). Lecture Notes in Computer Science 6475, 131–142. Springer.CrossRefGoogle Scholar
Wu, C., Aghajan, H. K. 2011. User-centric environment discovery with camera networks in smart homes. IEEE Transactions on Systems, Man, and Cybernetics, Part A 41(2), 375–383.CrossRefGoogle Scholar
Xu, M., Petrou, M. 2009. Learning logic rules for scene interpretation based on Markov logic networks. In ACCV (3), Zha, H., ichiro Taniguchi, R. & Maybank, S. J. (eds). Lecture Notes in Computer Science 5996, 341–350. Springer.CrossRefGoogle Scholar
Yoshida, M., Iizuka, T., Shiohara, H., Ishiguro, M. 2000. Mining sequential patterns including time intervals. In Data Mining and Knowledge Discovery, Dasarathy, B. V. (ed.). 213–220.Google Scholar