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Network Methods and Plan Recognition for Fusion in Crisis Management

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Fusion Methodologies in Crisis Management

Abstract

Building and updating a situational picture of the scenario under consideration is the goal of the Situation Assessment (SA) Information Fusion (IF) process. The scenario generally involves multiple entities and actors where possibly only a few under direct control of the decision maker. SA aims at explaining the observed events (mainly) by establishing the entities and actors involved, inferring their goals, understanding the relations existing (whether permanently or temporarily) between them, the surrounding environment, and past and present events. It is therefore apparent how the SA process inherently hinges on understanding and reasoning about relations. SA is a necessary preparatory step to the following phase of Impact Assessment (IA) where the decision maker is interested in estimating the evolution of the situation and the possible outcomes, dangers and threats. SA and IA processes are particularly complex and critical for large-scale scenarios with nearly chaotic dynamics such as those affected by natural or man-made disasters. This chapter will discuss recent developments in information fusion methods for representing and reasoning about relational information and knowledge for event detection in the context of crisis management. In particular, network methods will be analysed as a means for representing and reasoning about relational knowledge with the purpose of detecting complex events or discovering the causes of observed evidence.

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Notes

  1. 1.

    Although, this typically depends on the specific process and variables under consideration. For example, target tracking (filtering) can be performed in fine-grained time intervals since sensor observations can be frequent enough so to have a “continuous-like” response of the output. For higher level fusion processes, observations are typically much more coarse grained.

  2. 2.

    Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance.

  3. 3.

    One of the interests of the planning algorithms community is providing the most effective (in some sense) sequence of actions that would satisfy the user’s goals. This is particularly relevant for software agents or robotic systems where the software can perform actions that can modify the environment (software environment or the real word as in the case of robots). Here, the SA fusion system is mostly intended at situation monitoring and assessment, with limited direct capacity of modifying the real word (e.g. relocating information sources).

  4. 4.

    This is typical in surveillance and crisis management where the system is processing events as they occur. However, detection of patterns could be performed even over past events, that is mining offline data in search for relevant patterns in a forensic fashion.

  5. 5.

    The relationship just described is assumed to be of causal type where the cause C is known to produce the effect E. C ⇒ E is then a rule expressing a causal relationship between the antecedent and the consequent. This is a typical condition when the KB is built from experts’ knowledge that usually express rules in terms of causation giving to the rules much more strength and semantics than it would happen if the rules were to be learned automatically (in this latter case only correlation can be learned).

References

  • Allouche MK, Boukhtouta A (2010) Multi-agent coordination by temporal plan fusion: application to combat search and rescue. Inf Fusion 11(3):220–232

    Article  Google Scholar 

  • Ao Z, Scholz J, Oxenham M (2014) A scientific inquiry fusion theory for high-level information fusion. In: Proceedings of the 17th International Conference on Information Fusion, Salamanca

    Google Scholar 

  • Avrahami-Zilberbrand D, Kaminka GA (2014) Chapter 4 - keyhole adversarial plan recognition for recognition of suspicious and anomalous behavior. In: Sukthankar G, Goldman RP, Geib C, Pynadath DV, Bui HH (eds) Plan, activity, and intent recognition. Morgan Kaufmann, Boston, pp 87–119. http://dx.doi.org/10.1016/B978-0-12-398532-3.00004-X. http://www.sciencedirect.com/science/article/pii/B978012398532300004X

    Google Scholar 

  • Bharathan V, Josephson JR (2006) An abductive framework for level one information fusion. In: Proceedings of the 9th International Conference on Information Fusion, Florence

    Google Scholar 

  • Biermann J, Horling P, Snidaro L (2013) Experiences and challenges in automated support for intelligence in asymmetric operations. J Adv Inf Fusion 8(2):101–118

    Google Scholar 

  • Biermann J, Nimier V, Garcia J, Rein K, Krenc K, Snidaro L (2014) Multi-level fusion of hard and soft information. In: Proceedings of the 17th International Conference on Information Fusion. ISIF, Salamanca

    Google Scholar 

  • Biggs N, Lloyd EK, Wilson RJ (1976) Graph theory 1736–1936. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Coffman T, Greenblatt S, Marcus S (2004) Graph-based technologies for intelligence analysis. Commun ACM 47(3):45–47

    Article  Google Scholar 

  • de Kleer J (1986) An assumption-based {TMS}. Artif Intell 28(2):127–162. http://dx.doi.org/10.1016/0004-3702(86)90080-9. http://www.sciencedirect.com/science/article/pii/0004370286900809

  • de Vries G, Malaisé V, van Someren M, Adriaans P, Schreiber G (2008) Semi-automatic ontology extension in the maritime domain. In: Proceedings of twentieth Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2008)

    Google Scholar 

  • DiBona P, Belov N, Pawlowski A (2006) Plan-driven fusion: sha** the situation awareness process using empirical plan data. In: Proceedings of the 9th International Conference on Information Fusion. ISIF, Florence

    Google Scholar 

  • Ferrin G, Snidaro L, Canazza S, Foresti G (2008) Soft data issues in fusion of video surveillance. In: Proceedings of the Eleventh International Conference on Information Fusion, Cologne, pp 1882–1889

    Google Scholar 

  • Ferrin G, Snidaro L, Foresti GL (2010) Revisiting the role of abductive inference in fusion domain. In: 13th Conference on Information Fusion. IEEE, New York

    Google Scholar 

  • Getoor L, Taskar B (eds) (2007) Statistical relational learning. Adaptive computation and machine learning. MIT, Cambridge

    MATH  Google Scholar 

  • Hall D, McNeese M, Llinas J, Mullen T (2008) A framework for dynamic hard/soft fusion. In: Proceedings of the 11th International Conference on Information Fusion, Cologne

    Google Scholar 

  • Hanson NR (1958) Patterns of discovery. Cambridge University Press, Cambridge

    Google Scholar 

  • Harman GH (1965) The inference to the best explanation. Philos Rev 74(1):88–95

    Article  MathSciNet  Google Scholar 

  • Hristidis V, Chen SC, Li T, Luis S, Deng Y (2010) Survey of data management and analysis in disaster situations. J Syst Softw 83:1701–1714

    Article  Google Scholar 

  • Josephson JR (2008) Abductive inferencing for integrating information from human and robotic sources. In: Proceedings of the 11th International Conference on Information Fusion, Cologne, pp 109–114

    Google Scholar 

  • Lambert DA (2006) A unification of sensor and higher-level fusion. In: Proceedings of the 9th international conference on Information Fusion, Florence

    Google Scholar 

  • Li X, Porikli F (2004) A hidden Markov model framework for traffic event detection using video features. In: International Conference on Image Processing, 2004 (ICIP ’04), vol 5, pp 2901–2904

    Google Scholar 

  • Little EG, Rogova GL (2005) Ontology meta-model for building a situational picture of catastrophic events. In: Proceedings of the Eighth international conference on Information Fusion. International Society of Information Fusion, Philadelphia

    Book  Google Scholar 

  • Llinas J (2002) Fusion for natural and man-made disasters. In: Proceedings of the fifth International Conference on Information Fusion. International Society of Information Fusion, Annapolis

    Google Scholar 

  • Llinas J, Bowman CL, Rogova GL, Steinberg AN, Waltz EL, White FE (2004) Revisiting the JDL data fusion model II. In: Proceedings of the Seventh International Conference on Information Fusion, Stockholm, vol II, pp 1218–1230

    Google Scholar 

  • Möller R, Neumann B (2008) Ontology-based reasoning techniques for multimedia interpretation and retrieval. In: Semantic multimedia and ontologies: theory and applications. Springer, New York

    Google Scholar 

  • Munson LM (2009) Looking for anomalies all in the wrong places. In: USNI Proceedings Magazines 135/7/1,277

    Google Scholar 

  • Newman M (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  Google Scholar 

  • Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Poole D (1993) Probabilistic horn abduction and Bayesian networks. Artif Intell 64(1):81–129

    Article  MATH  Google Scholar 

  • Raghavan S, Singla P, Mooney RJ (2014) Chapter 3 - plan recognition using statistical-relational models. In: Sukthankar G, Goldman RP, Geib C, Pynadath DV, Bui HH (eds) Plan, activity, and intent recognition. Morgan Kaufmann, Boston, pp 57–85. http://dx.doi.org/10.1016/B978-0-12-398532-3.00003-8. http://www.sciencedirect.com/science/article/pii/B9780123985323000038

  • Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62:107–136

    Article  Google Scholar 

  • Rogova GL (2009) Context-awareness in crisis management. In: Proceedings of the Military Communications Conference. IEEE, New York, pp 1–7

    Google Scholar 

  • Rogova GL, Scott PD, Lollett C, Mudiyanur R (2006) Reasoning about situations in the early post-disaster response environment. In: 9th International Conference on Information Fusion

    Google Scholar 

  • Russell SJ, Norvig P (2010) Artificial intelligence - a modern approach, 3rd edn., vol I-XVIII. Pearson Education, Upper Saddle River

    Google Scholar 

  • Sambhoos K, Llinas J, Little E (2008) Graphical methods for real-time fusion and estimation with soft message data. In: 11th International Conference on Information Fusion. IEEE, New York, pp 1–8

    Google Scholar 

  • Scott P, Rogova G (2004) Crisis management in a data fusion synthetic task environment. In: Proceedings of FUSION 2004

    Google Scholar 

  • Snidaro L, Belluz M, Foresti G (2007) Domain knowledge for surveillance applications. In: Proceedings of the Tenth International Conference on Information Fusion, Quebec

    Book  Google Scholar 

  • Snidaro L, Visentini I, Foresti G (2011) Data fusion in modern surveillance. Studies in computational intelligence, vol 336, chap. 1. Springer, Berlin, pp 1–21

    Google Scholar 

  • Snidaro L, García J, Corchado JM (2015a) Context-based information fusion. Inf Fusion 21:82–84. doi:10.1016/j.inffus.2014.02.001. Guest Editorial

    Article  Google Scholar 

  • Snidaro L, García J, Llinas J (2015b) Context-based information fusion: a survey and discussion. Inf Fusion 25:16–31. doi:10.1016/j.inffus.2015.01.002

    Article  Google Scholar 

  • Snidaro L, Visentini I, Bryan K (2015c) Fusing uncertain knowledge and evidence for maritime situational awareness via Markov logic networks. Inf Fusion 21:159–172. doi:10.1016/j.inffus.2013.03.004

    Article  Google Scholar 

  • Steinberg A, Rogova G (2008) Situation and context in data fusion and natural language understanding. In: Proceedings of the Eleventh International Conference on Information Fusion, Cologne

    Google Scholar 

  • Sukthankar G, Goldman RP, Geib C, Pynadath DV, Bui HH (eds) (2014) Plan, activity, and intent recognition. Morgan Kaufmann, Boston

    Google Scholar 

  • Vishwakarma S, Agrawal A (2013) A survey on activity recognition and behavior understanding in video surveillance. Vis Comput 29(10):983–1009. doi:10.1007/s00371-012-0752-6. http://dx.doi.org/10.1007/s00371-012-0752-6

    Google Scholar 

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Acknowledgements

This work was partially supported by ONRG Grant N62909-14-1-N061.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Udine, via delle Scienze, 206, Udine, Italy

    Lauro Snidaro & Ingrid Visentini

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  1. Lauro Snidaro
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  2. Ingrid Visentini
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Correspondence to Lauro Snidaro .

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Editors and Affiliations

  1. State University of New York at Buffalo, Buffalo, New York, USA

    Galina Rogova

  2. State University of New Yorkat Buffalo, Buffalo, New York, USA

    Peter Scott

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Snidaro, L., Visentini, I. (2016). Network Methods and Plan Recognition for Fusion in Crisis Management. In: Rogova, G., Scott, P. (eds) Fusion Methodologies in Crisis Management. Springer, Cham. https://doi.org/10.1007/978-3-319-22527-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-22527-2_14

  • Publisher Name: Springer, Cham

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