V. Akman, S. Erdogan, J. Lee, V. Lifschitz, T. et al., Representing the Zoo World and the Traffic World in the language of the Causal Calculator, Artificial Intelligence, vol.153, issue.1-2, pp.1-2, 2004.
DOI : 10.1016/j.artint.2003.08.002

J. Allen, Maintaining knowledge about temporal intervals, Communications of the ACM, vol.26, issue.11, pp.832-843, 1983.
DOI : 10.1145/182.358434

M. R. Alvarez, P. Félix, P. Cariñena, and A. Otero, A Data Mining Algorithm for Inducing Temporal Constraint Networks, International Conference on Information Processing and Management of Uncertainty (IPMU, pp.300-309, 2010.
DOI : 10.1007/978-3-642-14049-5_31

A. Arasu, S. Babu, and J. Widom, The CQL continuous query language: semantic foundations and query execution, The VLDB Journal, vol.Francisco, issue.1, pp.121-142, 2006.
DOI : 10.1007/s00778-004-0147-z

A. Artikis, A. Kukurikos, G. Paliouras, P. Karampiperis, and C. Spyropoulos, Final version of knowledge base of event definitions and reasoning algorithms for event recognition Available from the authors, 2011.

A. Artikis, G. Paliouras, F. Portet, and A. Skarlatidis, Logic-based representation, reasoning and machine learning for event recognition, Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS '10, pp.282-293, 2010.
DOI : 10.1145/1827418.1827471
URL : https://hal.archives-ouvertes.fr/hal-00959183

A. Artikis, M. Sergot, and G. Paliouras, A logic programming approach to activity recognition, Proceedings of the 2nd ACM international workshop on Events in multimedia, EiMM '10, 2010.
DOI : 10.1145/1877937.1877941

R. Biswas, S. Thrun, and K. Fujimura, Recognizing Activities with Multiple Cues, Workshop on Human Motion, pp.255-270, 2007.
DOI : 10.1007/978-3-540-75703-0_18

L. Callens, G. Carrault, M. Cordier, ´. E. Fromont, F. Portet et al., Intelligent adaptive monitoring for cardiac surveillance, Proceedings of European Conference on Artificial Intelligence (ECAI, pp.653-657, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00460683

G. Carrault, M. Cordier, R. Quiniou, W. , and F. , Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms, Artificial Intelligence in Medicine, vol.28, issue.3, pp.231-263, 2003.
DOI : 10.1016/S0933-3657(03)00066-6
URL : https://hal.archives-ouvertes.fr/inserm-00134396

I. Cervesato, M. Franceschet, and A. Montanari, Modal event calculi with preconditions, Proceedings of TIME '97: 4th International Workshop on Temporal Representation and Reasoning, pp.38-45, 1997.
DOI : 10.1109/TIME.1997.600780

I. Cervesato, M. Franceschet, and A. Montanari, The complexity of model checking in modal event calculi with quantifiers, Journal of Electronic Transactions on Artificial Intelligence, 1998.

I. Cervesato, M. Franceschet, and A. Montanari, A Guided Tour through some Extensions of the Event Calculus, Computational Intelligence, vol.16, issue.2, pp.307-347, 2000.
DOI : 10.1111/0824-7935.00115

I. Cervesato and A. Montanari, A calculus of macro-events: progress report, Proceedings Seventh International Workshop on Temporal Representation and Reasoning. TIME 2000, pp.47-58, 2000.
DOI : 10.1109/TIME.2000.856584

H. Chaudet, Extending the event calculus for tracking epidemic spread, Artificial Intelligence in Medicine, vol.38, issue.2, pp.137-156, 2006.
DOI : 10.1016/j.artmed.2005.06.001

F. Chesani, P. Mello, M. Montali, and P. Torroni, Commitment tracking via the reactive event calculus, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp.91-96, 2009.

L. Chittaro and M. Dojat, Using a general theory of time and change in patient monitoring: Experiment and evaluation, Computers in Biology and Medicine, vol.27, issue.5, pp.435-452, 1997.
DOI : 10.1016/S0010-4825(97)00014-0
URL : https://hal.archives-ouvertes.fr/inserm-00402432

L. Chittaro and A. Montanari, EFFICIENT TEMPORAL REASONING IN THE CACHED EVENT CALCULUS, Computational Intelligence, vol.4, issue.3, pp.359-382, 1996.
DOI : 10.1145/131295.131299

C. Choppy, O. Bertrand, and P. Carle, Coloured Petri Nets for Chronicle Recognition, Proceedings of the Ada-Europe International Conference on Reliable Software Technologies, pp.266-281, 2009.
DOI : 10.1007/3-540-60029-9_44

K. Clark, Negation as Failure, pp.293-322, 1978.
DOI : 10.1007/978-1-4684-3384-5_11

R. Craven, Execution mechanisms for the action language C+, 2006.

G. Cugola and A. Margara, Processing flows of information, ACM Computing Surveys, vol.44, issue.3, 2011.
DOI : 10.1145/2187671.2187677

P. Damlen, J. Wakefield, and S. Walker, Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.2, pp.331-344, 1999.
DOI : 10.1111/1467-9868.00179

L. De-raedt and K. Kersting, Probabilistic inductive logic programming. Probabilistic inductive logic programming: theory and applications, pp.1-27, 2008.

R. De-salvo-braz, E. Amir, R. , and D. , A survey of first-order probabilistic models, Studies in Computational Intelligence, pp.289-317, 2008.

R. Dechter, I. Meiri, and J. Pearl, Temporal constraint networks, Temporal constraint networks, pp.61-95, 1991.
DOI : 10.1016/0004-3702(91)90006-6

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, issue.1, pp.1-38, 1977.

M. Denecker, K. Belleghem, G. Duchatelet, F. Piessens, and D. Schreye, A realistic experiment in knowledge representation in open event calculus: protocol specification, Proceedings of Joint International Conference and Symposium on Logic Programming, pp.170-184, 1996.

M. Denecker and A. Kakas, Special issue: abductive logic programming, The Journal of Logic Programming, vol.44, issue.1-3, pp.1-3, 2000.
DOI : 10.1016/S0743-1066(99)00078-3

M. Denecker and A. Kakas, Abduction in Logic Programming, Lecture Notes in Computer Science, vol.2407, pp.99-134, 2002.
DOI : 10.1007/3-540-45628-7_16

P. Doherty, J. Gustafsson, L. Karlsson, and J. Kvarnström, (TAL) temporal action logics: Language specification and tutorial, Electronic Transactions on Artificial Intelligence, vol.2, pp.3-4, 1998.

P. Domingos and D. Lowd, Markov Logic: An Interface Layer for Artificial Intelligence, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.3, issue.1, 2009.
DOI : 10.2200/S00206ED1V01Y200907AIM007

C. Dousson, Alarm driven supervision for télécommunication network II ? on-line chronicle recognition, Annales des Telecommunication, vol.51, pp.9-10, 1996.

C. Dousson, Extending and unifying chronicle representation with event counters, Proceedings of European Conference on Artificial Intelligence (ECAI, pp.257-261, 2002.

C. Dousson and T. V. Duong, Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp.620-626, 1999.

C. Dousson, P. Gaborit, and M. Ghallab, Situation recognition: Representation and algorithms, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI, pp.166-174, 1993.

C. Dousson and P. L. Maigat, Improvement of chronicle-based monitoring using temporal focalization and hierarchisation, Proceedings of International Workshop on Principles of Diagnosis (DX), pp.257-261, 2006.

C. Dousson and P. L. Maigat, Chronicle recognition improvement using temporal focusing and hierarchisation, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI, pp.324-329, 2007.

C. Dousson, K. Pentikousis, T. Sutinen, and J. Mäkelä, Chronicle Recognition for Mobility Management Triggers, 2007 IEEE Symposium on Computers and Communications, pp.305-310, 2007.
DOI : 10.1109/ISCC.2007.4381530

S. Dzeroski and N. Lavrac, Relational Data Mining, 2001.

O. Etzion and P. Niblett, Event Processing in Action, 2010.

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, vol.14, issue.02n03, pp.2-3, 2005.
DOI : 10.1142/S0218843005001110

F. Fessant, F. Clérot, and C. Dousson, Mining of an Alarm Log to Improve the Discovery of Frequent Patterns, Industrial Conference on Data Mining, pp.144-152, 2004.
DOI : 10.1109/72.846731

F. Gao, Y. Sripada, J. Hunter, and F. Portet, Using Temporal Constraints to Integrate Signal Analysis and Domain Knowledge in Medical Event Detection, Artificial Intelligence in Medicine. LNCS 5651, pp.46-55, 2009.
DOI : 10.1016/j.artmed.2006.03.007
URL : https://hal.archives-ouvertes.fr/hal-00959217

L. Getoor and B. Taskar, Introduction to statistical relational learning, 2007.

M. Ghallab, On chronicles: Representation, on-line recognition and learning, Proceedings of Conference on Principles of Knowledge Representation and Reasoning, pp.597-606, 1996.

M. Ghallab and A. M. Alaoui, Managing efficiently temporal relations through indexed spanning trees, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp.1297-1303, 1989.

E. Giunchiglia, J. Lee, V. Lifschitz, N. Mccain, T. et al., Nonmonotonic causal theories, Artificial Intelligence, vol.153, issue.1-2, pp.1-2, 2004.
DOI : 10.1016/j.artint.2002.12.001

A. Hakeem and M. Shah, Learning, detection and representation of multi-agent events in videos, Artificial Intelligence, vol.171, issue.8-9, pp.8-9, 2007.
DOI : 10.1016/j.artint.2007.04.002

R. Helaoui, M. Niepert, and H. Stuckenschmidt, A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems, Advances in Soft Computing, pp.247-254, 2010.
DOI : 10.1007/978-3-642-13268-1_34

Y. Hirate and H. Yamana, Sequential Pattern Mining with Time Intervals, Advances in Knowledge Discovery and Data Mining, pp.775-779, 2006.
DOI : 10.1007/11731139_90

S. Hongeng and R. Nevatia, Large-scale event detection using semi-hidden markov models, Proceedings of Conference on Computer Vision. IEEE, pp.1455-1462, 2003.

T. Huynh and R. Mooney, Discriminative structure and parameter learning for Markov logic networks, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.416-423, 2008.
DOI : 10.1145/1390156.1390209

A. C. Kakas, R. A. Kowalski, T. , and F. , Abductive Logic Programming, Journal of Logic and Computation, vol.2, issue.6, pp.719-770, 1992.
DOI : 10.1093/logcom/2.6.719

A. Kembhavi, T. Yeh, D. , and L. S. , Why Did the Person Cross the Road (There)? Scene Understanding Using Probabilistic Logic Models and Common Sense Reasoning, ECCV, pp.693-706, 2010.
DOI : 10.1007/978-3-642-15552-9_50

K. Kersting, L. De-raedt, R. , and T. , Logical hidden markov models, Journal of Artificial Intelligence Research, vol.25, issue.1, pp.425-456, 2006.

T. Kohonen, Self-Organising Maps, 2001.

S. Kok and P. Domingos, Learning the structure of Markov logic networks, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.441-448, 2005.
DOI : 10.1145/1102351.1102407

S. Kok and P. Domingos, Learning Markov logic network structure via hypergraph lifting, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.505-512, 2009.
DOI : 10.1145/1553374.1553440

S. Kok and P. Domingos, Learning markov logic networks using structural motifs, Proceedings of the International Conference on Machine Learning (ICML), pp.551-558, 2010.

S. Konstantopoulos, R. Camacho, N. Fonseca, C. , and V. S. , Induction as a search, Artificial Intelligence for Advanced Problem Solving Techniques, D. Vrakas and I. Vlahavas, pp.158-205, 2008.

R. Kowalski and F. Sadri, Reconciling the event calculus with the situation calculus, The Journal of Logic Programming, vol.31, issue.1-3, pp.39-58, 1997.
DOI : 10.1016/S0743-1066(96)00137-9

R. Kowalski and M. Sergot, A logic-based calculus of events, New Generation Computing, vol.10, issue.No. 2, pp.67-96, 1986.
DOI : 10.1007/BF03037383

J. Kvarnström, TALplanner and other extensions to temporal action logic, 2005.

W. V. Laer, From propositional to first order logic in machine learning and data mining, 2002.

L. Guillou, X. Cordier, M. Robin, S. , R. et al., Chronicles for on-line diagnosis of distributed systems, Proceedings of European Conference on Artificial Intelligence (ECAI, pp.194-198, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00461386

D. Lowd and P. Domingos, Efficient weight learning for Markov logic networks. Knowledge Discovery in Databases: PKDD, pp.200-211, 2007.

D. Luckham, The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems, 2002.
DOI : 10.1007/978-3-540-88808-6_2

D. Luckham and R. Schulte, Event processing glossary ? version 1.1. Event Processing Technical Society, 2008.

F. Lv, R. Nevatia, and M. Lee, 3D Human Action Recognition Using Spatio-temporal Motion Templates, Proceedings of International Workshop on Computer Vision in Human-Computer Interaction (ICCV, pp.120-130, 2005.
DOI : 10.1007/11573425_12

A. Mackworth and E. Freuder, The complexity of some polynomial network consistency algorithms for constraint satisfaction problems, Artificial Intelligence, vol.25, issue.1, pp.65-74, 1985.
DOI : 10.1016/0004-3702(85)90041-4

H. Mannila, H. Toivonen, and A. I. Verkamo, Discovery of frequent episodes in event sequences, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.259-289, 1997.
DOI : 10.1023/A:1009748302351

J. Mccarthy and P. Hayes, Some Philosophical Problems from the Standpoint of Artificial Intelligence, Machine Intelligence, vol.4, pp.463-502, 1969.
DOI : 10.1016/B978-0-934613-03-3.50033-7

L. Mihalkova and R. Mooney, Bottom-up learning of Markov logic network structure, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.625-632, 2007.
DOI : 10.1145/1273496.1273575

R. Miller and M. Shanahan, The event calculus in a classical logic ? alternative axiomatizations, Journal of Electronic Transactions on Artificial Intelligence, vol.3, pp.77-105, 1999.

R. Miller and M. Shanahan, Some Alternative Formulations of the Event Calculus, Computational Logic: Logic Programming and Beyond ? Essays in Honour of Robert A. Kowalski. LNAI 2408. Spr, pp.452-490, 2002.
DOI : 10.1007/3-540-45632-5_17

B. Morin and H. Debar, Correlation of Intrusion Symptoms: An Application of Chronicles, 6th International Conference on Recent Advances in Intrusion Detection (RAID'03), 2003.
DOI : 10.1007/978-3-540-45248-5_6

S. Moyle, Using Theory Completion to Learn a Robot Navigation Control Program, In Inductive Logic Programming, vol.2583, pp.182-197, 2002.
DOI : 10.1007/3-540-36468-4_12

E. Mueller, Commonsense Reasoning, 2006.

E. Mueller, Event calculus and temporal action logics compared, Artificial Intelligence, vol.170, issue.11, pp.1017-1029, 2006.
DOI : 10.1016/j.artint.2006.05.001

S. Muggleton, Inductive logic programming, New Generation Computing, vol.7, issue.1, pp.295-318, 1991.
DOI : 10.1007/BF03037089

S. Muggleton, Inverse entailment and progol, New Generation Computing, vol.12, issue.1, pp.3-4, 1995.
DOI : 10.1007/BF03037227
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1630

S. Muggleton and C. Bryant, Theory Completion Using Inverse Entailment, In Inductive Logic Programming, vol.1866, pp.130-146, 2000.
DOI : 10.1007/3-540-44960-4_8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.5787

S. Muggleton and L. D. Raedt, Inductive Logic Programming: Theory and methods, The Journal of Logic Programming, vol.19, issue.20, pp.629-679, 1994.
DOI : 10.1016/0743-1066(94)90035-3
URL : http://doi.org/10.1016/0743-1066(94)90035-3

K. Murphy, Dynamic bayesian networks: representation, inference and learning, 2002.

B. Nebel and H. Bürckert, Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra, Journal of the ACM, vol.42, issue.1, pp.43-66, 1995.
DOI : 10.1145/200836.200848

C. Nédellec, C. Rouveirol, H. Adé, F. Bergadano, T. et al., Declarative bias in ILP, Advances in Inductive Logic Programming, L. D. Raedt, pp.82-103, 1996.

N. Nguyen, D. Phung, S. Venkatesh, and H. Bui, Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.203

A. Paschke, ECA-RuleML: An approach combining ECA rules with temporal interval-based KR event/action logics and transactional update logics, 2005.

A. Paschke, ECA-LP/ECA-RuleML: A homogeneous event-condition-action logic programming language, Tech. rep, p.609143, 2006.

A. Paschke and M. Bichler, Knowledge representation concepts for automated SLA management, Decision Support Systems, vol.46, issue.1, pp.187-205, 2008.
DOI : 10.1016/j.dss.2008.06.008

A. Paschke and A. Kozlenkov, Rule-Based Event Processing and Reaction Rules, Proceedings of RuleML, pp.53-66, 2009.
DOI : 10.1007/978-3-642-04985-9_8

A. Paschke, A. Kozlenkov, and H. Boley, A homogeneous reaction rule language for complex event processing, Proceedings of International Workshop on Event-driven Architecture, Processing and Systems, 2007.

H. Poon and P. Domingos, Sound and efficient inference with probabilistic and deterministic dependencies, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, 2006.

H. Poon and P. Domingos, Joint unsupervised coreference resolution with Markov logic, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.650-659, 2008.
DOI : 10.3115/1613715.1613796

J. Pottebaum and R. Marterer, 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, 2010.

J. R. Quinlan, C. , and R. M. , Induction of logic programs: FOIL and related systems, New Generation Computing, vol.5, issue.1, pp.287-312, 1995.
DOI : 10.1007/BF03037228

L. Rabiner and B. Juang, A tutorial on hidden Markov models, Proceedings of the IEEE 77, pp.257-286, 1989.

O. Ray, Nonmonotonic abductive inductive learning, Journal of Applied Logic, vol.7, issue.3, pp.329-340, 2009.
DOI : 10.1016/j.jal.2008.10.007

R. Reiter, Knowledge in Action: Logical Foundations for Describing and Implementing Dynamical Systems, 2001.

F. Sadri and R. Kowalski, Variants of the event calculus, Proceedings of the International Conference on Logic Programming, pp.67-81, 1995.

M. Shanahan, The Event Calculus Explained, LNAI 1600, pp.409-430, 1999.
DOI : 10.1016/S0004-3702(96)00033-1

V. Shet, D. Harwood, D. , and L. , VidMAP: video monitoring of activity with prolog, Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005., pp.224-229, 2005.
DOI : 10.1109/AVSS.2005.1577271

V. Shet, D. Harwood, D. , and L. , Multivalued Default Logic for Identity Maintenance in Visual Surveillance, Proceedings of European Conference on Computer Vision (ECCV). LNCS 3954, pp.119-132, 2006.
DOI : 10.1007/11744085_10

V. Shet, J. Neumann, V. Ramesh, D. , and L. , Bilattice-based Logical Reasoning for Human Detection, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383133

P. Singla and P. Domingos, Discriminative training of Markov logic networks, Proceedings of the AAAI Conference on Artificial Intelligence, pp.868-873, 2005.

P. Singla and P. Domingos, Memory-efficient inference in relational domains, Proceedings of the AAAI Conference on Artificial Intelligence, 2006.

P. Singla and P. Domingos, Lifted first-order belief propagation, Proceedings of the AAAI Conference on Artificial Intelligence, pp.1094-1099, 2008.

A. Tamaddoni-nezhad, R. Chaleil, A. C. Kakas, and S. Muggleton, Application of abductive ILP to learning metabolic network inhibition from temporal data, Machine Learning, vol.19, issue.3, pp.1-3, 2006.
DOI : 10.1007/s10994-006-8988-x

K. Teymourian and A. Paschke, Semantic Rule-Based Complex Event Processing, Proceedings of RuleML, pp.82-92, 2009.
DOI : 10.1007/978-3-642-04985-9_10

M. Thielscher, From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem, Artificial Intelligence, vol.111, issue.1-2, pp.1-2, 1999.
DOI : 10.1016/S0004-3702(99)00033-8

M. Thielscher, The Qualification Problem: A solution to the problem of anomalous models, Artificial Intelligence, vol.131, issue.1-2, pp.1-2, 2001.
DOI : 10.1016/S0004-3702(01)00131-X

M. Thonnat, Semantic activity recognition, Proceedings of European Conference on Artificial Intelligence (ECAI, pp.3-7, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00502249

S. D. Tran and L. S. Davis, Event Modeling and Recognition Using Markov Logic Networks, Proceedings of Computer Vision Conference, pp.610-623, 2008.
DOI : 10.1007/978-3-540-88688-4_45

A. Vautier, M. Cordier, and R. Quiniou, Towards Data Mining Without Information on Knowledge Structure, In Knowledge Discovery in Databases, pp.300-311, 2007.
DOI : 10.1007/978-3-540-74976-9_29
URL : https://hal.archives-ouvertes.fr/inria-00463005

M. B. Vilain and H. A. Kautz, Constraint propagation algorithms for temporal reasoning, Proceedings of the AAAI Conference on Artificial Intelligence, pp.377-382, 1986.

V. Vu, F. Brémond, and M. Thonnat, Automatic video interpretation: A novel algorithm for temporal scenario recognition, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI, pp.1295-1302, 2003.

B. R. Wellner, An abductive-inductive learning framework for logic-based agents, 1999.

C. Wu and H. K. Aghajan, Recognizing Objects in Smart Homes Based on Human Interaction, Lecture Notes in Computer Science, vol.6475, issue.2, pp.131-142, 2010.
DOI : 10.1007/978-3-642-17691-3_13

C. Wu and H. K. Aghajan, User-Centric Environment Discovery With Camera Networks in Smart Homes, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.41, issue.2, pp.375-383, 2011.
DOI : 10.1109/TSMCA.2010.2073701

M. Xu and M. Petrou, Learning Logic Rules for Scene Interpretation Based on Markov Logic Networks, ACCV, pp.341-350, 2009.
DOI : 10.1007/978-3-642-12297-2_33

M. Yoshida, T. Iizuka, H. Shiohara, and M. Ishiguro, Mining sequential patterns including time intervals, Data Mining and Knowledge Discovery, pp.213-220, 2000.