Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks

Information Fusion

Volumn 21, Issue 1, 2015, Pages 159-172

  Snidaro, Lauro ^a   Visentini, Ingrid ^b   Bryan, Karna ^b  

^a UNIVERSITY OF UDINE   (Italy)

^b NATO UNDERSEA RESEARCH CENTRE   (Italy)

Author keywords

Context based fusion; Markov Logic Networks; Situational awareness; Uncertainty management

Indexed keywords

MARKOV PROCESSES;

CONTEXT-BASED; LOGICAL COMBINATION; MARKOV LOGIC NETWORKS; PROBABILISTIC UNCERTAINTY; SITUATION ASSESSMENT; SITUATIONAL AWARENESS; UNCERTAIN KNOWLEDGE; UNCERTAINTY MANAGEMENT;

COMPLEX NETWORKS;

EID: 84904793445     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2013.03.004     Document Type: Article

References (42)

1. L. Snidaro, I. Visentini, and G. Foresti Studies in Computational Intelligence vol. 336/2011 2011 Springer-Verlag (Chapter Data fusion in modern surveillance)̀
2. B. Ristic, B. La Scala, M. Morelande, N. Gordon, Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction, in: Proceedings of the 11th International Conference on Information Fusion, 2008.
3. C. Carthel, S. Coraluppi, P. Grignan, Multisensor tracking and fusion for maritime surveillance, in: Proceedings of the 10th International Conference on Information Fusion, 2007.
4. M. Riveiro, G. Falkman, T. Ziemke, T. Kronhamn, Reasoning about anomalies: a study of the analytical process of detecting and identifying anomalous behavior in maritime traffic data, in: Proceedings of SPIE Defense, Security, and Sensing 2009 SPIE, vol. 7346, 73460A.
5. C. Brax, L. Niklasson, Enhanced situational awareness in the maritime domain: An agent-based approach for situation management, in: Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing: Proceedings of SPIE Defense, Security, and Sensing 2009, SPIE, 2009.
6. R. Lane, D. Nevell, S. Hayward, T. Beaney, Maritime anomaly detection and threat assessment, in: Proceedings of the 13th International Conference on Information Fusion, 2010.
7. R. Carvalho, R. Haberlin, P. Costa, K. Laskey, K. Chang, Modeling a probabilistic ontology for maritime domain awareness, in: Proceedings of the 14th International Conference on Information Fusion, 2011.
8. J. Roy, Automated reasoning for maritime anomaly detection, in: Proceedings of the NATO Workshop on Data Fusion and Anomaly Detection for Maritime Situational Awareness (MSA 2009), NATO Undersea Research Centre (NURC), La Spezia, Italy, 2009.
9. J. Roy, Rule-based expert system for maritime anomaly detection, in: Proceedings of the 12th International Conference on Information Fusion, 2009.
10. J. Roy, M. Davenport, Exploitation of maritime domain ontologies for anomaly detection and threat analysis, in: Waterside Security Conference (WSS), 2010 International, 2010.
11. S.J. Russell, and P. Norvig 3rd ed. Artificial Intelligence - A Modern Approach vol. I-XVIII 2010 Pearson Education
12. J. Llinas, C.L. Bowman, G.L. Rogova, A.N. Steinberg, E.L. Waltz, F.E. White, Revisiting the JDL data fusion model II, in: Proceedings of the Seventh International Conference on Information Fusion, vol. II, Stockholm, Sweden, 2004, pp. 1218-1230.
13. L.M. Munson, Looking for anomalies all in the wrong places, in: USNI Proceedings Magazines 135/7/1,277.
14. I. Visentini, L. Snidaro, Integration of contextual information for tracking refinement, in: 14th International Conference on Information Fusion, Chicago, Illinois, 2011.
15. C. Sodtke, J. Llinas, Terrain based tracking using position sensors, in: International Conference on Information Fusion, vol. 2, 2001, pp. 27-32.
16. V. Nimier, Supervised multisensor tracking algorithm, in: International Conference on Multisource-Multisensor Information Fusion, Las Vegas, USA, 1998, pp. 149-156.
17. J. Roy, M. Davenport, Categorization of maritime anomalies for notification and alerting purpose, in: NATO Workshop on Data Fusion and Anomaly Detection for Maritime Situational Awareness, La Spezia, Italy, 2009.
18. M. Richardson, and P. Domingos Markov logic netwoks Machine Learning 62 2006 107 136
19. L. Snidaro, I. Visentini, K. Bryan, G.L. Foresti, Markov logic networks for context integration and situation assessment in maritime domain, in: Proceedings of the 15th International Conference on Information Fusion, Singapore, 2012, pp. 1534-1539.
20. L. Snidaro, M. Belluz, G. Foresti, Domain knowledge for surveillance applications, in: Proceedings of the Tenth International Conference on Information Fusion, Quebec City, Canada, 2007.
21. W.R. van Hage, V. Malaise, R.H. Segers, L. Hollink, G. Schreiber, Design and use of the simple event model (SEM), Web Semantics: Science, Services and Agents on the World Wide Web 9(2).
22. J. Roy, Anomaly detection in the maritime domain, in: Optics and Photonics in Global Homeland Security IV. Proceedings of the SPIE, vol. 6945, 2008, pp. 69450W-69450W-14.
23. L. Snidaro, C. Piciarelli, G.L. Foresti, Fusion of trajectory clusters for situation assessment, in: Proceedings of the Ninth International Conference on Information Fusion, Florence, Italy, 2006.
24. E.H. Shortliffe, and B.G. Buchanan A model of inexact reasoning in medicine Mathematical Biosciences 23 3-4 1975 351 379
25. D.A. Waterman A Guide to Expert Systems 1985 Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA
26. G. Rogova, E. Bossé, Information quality in information fusion, in: Proc. of the 13th. International Conference on Information Fusion, Edinburgh, U.K., 2010.
27. T. Lukasiewicz, and U. Straccia Managing uncertainty and vagueness in description logics for the semantic web Journal of Web Semantics 6 4 2008 291 308
28. P. Costa, R. Carvalho, K. Laskey, C. Park, Evaluating uncertainty representation and reasoning in HLF systems, in: Proceedings of the Fourteenth International Conference on Information Fusion (Fusion 2011), Chicago, IL, USA, 2011.
29. A. Patcha, and J. Park An overview of anomaly detection techniques: existing solutions and latest technological trends Computer Networks 51 2007 3448 3470 (Pubitemid 46921030)
30. X. Li, F. Porikli, A hidden markov model framework for traffic event detection using video features, in: International Conference on Image Processing (ICIP), vol. 5, 2004, pp. 2901-2904. (Pubitemid 40820680)
31. G. de Vries, V. Malaisn++, M. van Someren, P. Adriaans, G. Schreiber, Semi-automatic ontology extension in the maritime domain, in: Proceedings of twentieth Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2008), 2008.
32. J. Garcia, J. Gomez-Romero, M. Patricio, J. Molina, G. Rogova, On the representation and exploitation of context knowledge in a harbor surveillance scenario, in: Proceedings of the 14th International Conference on Information Fusion, 2011.
33. K.B. Laskey, R. Haberlin, R.N. Carvalho, P.C.G. Costa, Pr-owl 2 case study: a maritime domain probabilistic ontology, in: P. Costa, K. Laskey, (Eds.), Proceedings of the 6th International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS 2011), Fairfax, VA, USA, 2011, pp. 76-83.
34. R. Orchard, Fuzzy reasoning in JESS: the Fuzzyj Toolkit and Fuzzyjess, in: Proceedings of the Third International Conference on Enterprise Information Systems (ICEIS), Setubal, Portugal, 2001, pp. 533-542.
35. F. Smarandache, and J. Dezert Advances and Applications of DSmT for Information Fusion: Collected works vol. 3 2009 American Research Press
36. J. Balmat, F. Lafont, R. Maifret, and N. Pessel A decision-making system to maritime risk assessment Ocean Engineering 38 1 2011 171 176
37. T. Imkamon, P. Saensom, P. Tangamchit, P. Pongpaibool, Detection of hazardous driving behavior using fuzzy logic, in: 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008, ECTI-CON 2008, vol. 2, 2008, pp. 657 -660.
38. L. Getoor, and B. Taskar Statistical Relational Learning, Adaptive Computation and Machine Learning 2007 MIT Press Cambridge, Massachussets
39. S.D. Tran, and L.S. Davis Event modeling and recognition using markov logic networks Proceedings of the European Conference on Computer Vision (ECCV) vol. 5303 2008 Springer Berlin/Heidelberg 610 623
40. D. Hall, M. McNeese, J. Llinas, T. Mullen, A framework for dynamic hard/soft fusion, in: Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany, 2008.
41. J.F. Allen Maintaining knowledge about temporal intervals Communications of the ACM 1983 832 843
42. G. Priest Handbook of Philosophical Logic vol. 6 2002 Kluwer Chapter Paraconsistent logic