High-Level Fusion for Crisis Response Planning

  • Kathryn B. Laskey
  • Henrique C. Marques
  • Paulo C. G. da Costa


Each year, natural and anthropogenic crises disrupt the lives of millions of people. Local, national, and international crisis response systems struggle to cope with urgent needs during and immediately after a crisis. The challenges multiply as population grows, density of urban areas increases, and coastal areas become more vulnerable to rising sea levels. A typical crisis scenario requires coordinating many diverse players, including local, national and international military, other governmental, and non-governmental organizations. Often, no single entity is in charge of the response, making coordination even more difficult. There is an urgent need for better ways to allocate resources, maintain situation awareness, and reallocate resources as the situation changes. Information fusion is vital to effective resource allocation and situation awareness. Some of the greatest inefficiencies stem from the inability to exchange information between systems designed for different purposes and operating under different ownership. Information integration and fusion are too often entirely manual. Greater automation could support more timely, better coordinated responses in situations where time is of the essence. The greatest need is not for low-level fusion of sensor reports to classify and track individual objects, but for high-level fusion to characterize complex situations and support planning of effective responses. This paper describes challenges of high-level fusion for crisis management, proposes a technical framework for addressing high-level fusion, and discusses how effectively addressing HLF challenges can improve efficiency of crisis response. We illustrate our ideas with a case study involving a humanitarian relief operation in a flooding scenario.


  1. Bajoria J (2011) Improving UN responses to humanitarian crises. UN Chron XLVIII(4)Google Scholar
  2. Carey S, Kleiner M, Hieb M, Brown R (2001) Standardizing battle management language—a vital move towards the army transformation. In: IEEE fall simulation interoperability workshop, OrlandoGoogle Scholar
  3. Carvalho R (2011) Probabilistic ontology: representation and modeling methodology. Ph.D. dissertation, Systems Engineering and Operations Research, George Mason UniversityGoogle Scholar
  4. da Costa PCG (2005) Bayesian semantics for the semantic web. Ph.D. dissertation, Information Technology, George Mason UniversityGoogle Scholar
  5. da Costa PCG, Ladeira M, Carvalho RN, Laskey KB, Santos LL, Matsumoto S (2008) A first-order Bayesian tool for probabilistic ontologies. In: FLAIRS conference, pp 631–636Google Scholar
  6. Ding Z (2005) BayesOWL: a probabilistic framework for semantic web. Ph.D. dissertation, Science of Computation, University of MarylandGoogle Scholar
  7. Edelkamp S, Hoffmann J (2004) PDDL2.2: The language for the classical. In: 4th International planning competition, 2004, proceedings[S.l.:s.n], edn. Bibliography, p 118Google Scholar
  8. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors J Hum Factors Ergon Soc 37:32–64(33). doi:10.1518/001872095779049543. http://www.ingentaconnect.com/content/hfes/hf/1995/00000037/00000001/art00004
  9. Federal Emergency Management Agency (2014) National preparedness cycle. https://www.fema.gov/national-preparedness-cycle. Accessed 30 Dec 2014
  10. Federal Emergency Management Agency (2014) Private section fact sheet. http://www.fema.gov/pdf/privatesector/saver_factsheet.pdf. Accessed 30 Dec 2014
  11. Fox M, Long D (2003) PDDL2.1: An extension to PDDL for expressing temporal planning domains. J Artif Intell Res 20(1):61–124MATHGoogle Scholar
  12. Franklin E, White J (1987) Data fusion lexicon. Technical report, Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel, Naval Ocean Systems Center, San DiegoGoogle Scholar
  13. Gerevini A, Long D (2005) Plan constraints and preferences in PDDL3. In: 5th International planning competition, 2005, proceedings[S.l.:s.n], edn.Google Scholar
  14. Ghallab M, Nau D, Traverso P (2004) Automated planning theory and practice. Elsevier/Morgan Kaufmann, AmsterdamGoogle Scholar
  15. Goldman RP (2004) Ontologies and planners a statement of interest. In: AAAI spring symposium. Technical report, vol 4, pp 103–105Google Scholar
  16. Horridge M, Patel-Schneider P (2012) OWL 2 web ontology language Manchester syntax, 2nd edn. http://www.w3.org/2007/OWL/wiki/ManchesterSyntax. Accessed 05 Jan 2015
  17. Hughes DJ (ed) (1993) Moltke on the art of war: selected writings. Presidio Press, New YorkGoogle Scholar
  18. Humphries V (2013) Improving humanitarian coordination: common challenges and lessons learned from the cluster approach. J Humanit Assist. http://sites.tufts.edu/jha/archives/1976
  19. Inter-Agency Standing Committee (2006) IASC guidance note on using the cluster approach to strengthen humanitarian response. Technical report. http://www.refworld.org/docid/460a8ccc2.html. Accessed 28 Dec 2014
  20. Inter-Agency Standing Committee (2013) Official site. http://www.humanitarianinfo.org/iasc/. Accessed 28 Dec 2014
  21. Laskey KB (2008) MEBN: A language for first-order Bayesian knowledge bases. Artif Intell 172(2–3):140–178CrossRefMathSciNetMATHGoogle Scholar
  22. Lewis G, Lander B (2011) Only as strong as our weakest link: can the humanitarian system be collectively accountable to affected populations? Humanit Exch Mag 52Google Scholar
  23. Marques HC (2012) inference model with probabilistic ontologies to support automation in effects-based operations planning. Ph.D. thesis, Instituto Tecnológico de Aeronáutica, São José dos CamposGoogle Scholar
  24. Marques HC, de Oliveira JMP, da Costa PCG (2011) Representing COA with probabilistic ontologies. In: Proceedings of the international command and control research and technology symposiumGoogle Scholar
  25. Martin SF, Weerasinghe S, Taylor A (eds) (2014) Humanitarian crises and migration: causes, consequences and responses. Routledge, New YorkGoogle Scholar
  26. McDermott D, Ghallab M, Howe A, Knoblock C, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL—the planning domain definition language, version 1.2. Technical report, TR-98-003, Yale Center for Computational Vision and Control. http://homepages.inf.ed.ac.uk/mfourman/tools/propplan/pddl.pdf. Accessed 03 Jan 2015
  27. Nau D, Ilghami O, Kuter U, Murdock JW, Wu D, Yaman F (2003) SHOP2: an HTN planning system. J Artif Intell Res 20:379–404MATHGoogle Scholar
  28. Newman W (1999) Steel bank common Lisp (SBCL). http://www.sbcl.org/. Accessed 2015-01-03
  29. Pednault EPD (1989) ADL: exploring the middle ground between STRIPS and the situation calculus. In: Proceedings of the international conference on principles of knowledge representation and reasoning. Morgan Kaufmann Publishing, San Francisco, pp 324–332Google Scholar
  30. Predoiu L, Stuckenschmidt H (2008) Probabilistic extensions of semantic web languages: a survey. In: Ma Z, Wang H (eds) The semantic web for knowledge and data management: technologies and practices. Idea Group, HersheyGoogle Scholar
  31. Rasmussen J (1993) Deciding and doing: decision-making in natural contexts. In: Klein G et al (ed) Decision-making in action: models and methods. Ablex, NorwoodGoogle Scholar
  32. Saucedo AA (2013) OODA (observe, orient, decide, and act). In: Piehler GK (ed) Encyclopedia of military science, 0 edn. SAGE Publications, Thousand Oaks, pp 1016–1019. doi:http://dx.doi.org/10.4135/9781452276335. Accessed 04 Jan 2015
  33. Schade U, Hieb MR (2006) Formalizing battle management language: a grammar for specifying orders. In: IEEE spring simulation interoperability workshopGoogle Scholar
  34. Schade U, Hieb MR (2007) Improving planning and replanning: using a formal grammar to automate processing of command and control information for decision support. Int C2 J 1(2):69–90Google Scholar
  35. Seybolt TB (2000) Systemic network analysis of refugee relief: a business-like approach. Text. http://reliefweb.int/report/world/systemic-network-analysis-refugee-relief-business-approach
  36. Simulation Interoperability Standards Organization (2012) Official site. http://www.sisostds.org/Home.aspx. Accessed 03 Jan 2015
  37. Smith EA (2002) Effects based operations: applying network centric warfare in peace, crisis and war. CCRP, AlexandriaGoogle Scholar
  38. Stanford University (2009) Description logic query language: Dl query. http://protegewiki.stanford.edu/wiki/DLQueryTab. Accessed 02 Jan 2015
  39. Stanford University (2012) Protégé project. http://protege.stanford.edu. Accessed 02 Jan 2015
  40. Steinberg AN, Bowman CL, White FE (1999) Revisions to the jdl data fusion model. doi:10.1117/12.341367. http://dx.doi.org/10.1117/12.341367
  41. Street AM (2009) Humanitarian reform a progress report. Humanit Exch Mag 45Google Scholar
  42. Ubuntu (2015) Ubuntu OS. Available at: http://www.ubuntu.com/. Accessed 03 Jan 2015
  43. Ushahidi (2008) Official site. www.ushahidi.com. Accessed 27 Dec 2014
  44. VMWare (2014) Official site. http://www.vmware.com. Accessed 03 Jan 2015
  45. VT MÄK (2012) VR-Forces. http://www.mak.com/. Accessed 03 Jan 2015
  46. W3C (2004) OWL web ontology language. http://www.w3.org/TR/owl-features. Accessed 01 Jan 2015
  47. W3C (2009) OWL 2 web ontology language document overview. http://www.w3.org/TR/owl2-overview/. Accessed 01 Jan 2015
  48. Yang Q (1997) Intelligent planning: a decomposition and abstraction based approach. Springer, LondonCrossRefMATHGoogle Scholar
  49. Yonetani M (2014) Global estimates 2014: people displaced by disasters. Technical report, Norwegian Refugee Council. http://reliefweb.int/sites/reliefweb.int/files/resources/201409-global-estimates.pdf. Accessed 27 Dec 2014
  50. Younes H, Littman M (2004) PPDDL: the probabilistic planning domain definition language. http://www.cs.cmu.edu/. Accessed 03 Jan 2015

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kathryn B. Laskey
    • 1
  • Henrique C. Marques
    • 2
  • Paulo C. G. da Costa
    • 1
  1. 1.Department of Systems Engineering and Operations Research and C4I CenterGeorge Mason UniversityFairfaxUSA
  2. 2.Aeronautics Institute of Technology - ITASão José dos CamposBrazil

Personalised recommendations