Advertisement

Meta-modeling the Cultural Behavior Using Timed Influence Nets

  • Faisal Mansoor
  • Abbas K. Zaidi
  • Lee Wagenhals
  • Alexander H. Levis
Conference paper

Abstract

A process that can be used to assist analysts in developing domain specific Timed Influence Nets (TIN) is presented. The process can be used to represent knowledge about a situation that includes descriptions of cultural behaviors and actions that may influence such behaviors. One of the main challenges in using TINs has been the difficulty in formulating them. Many Subject Matter Experts have difficulty in expressing their knowledge in the TIN representation. The ontology based meta modeling approach described in this paper provides potential assistance to these modelers so that they can quickly create new models for new situations and thus can spend more time doing analysis. The paper describes the theoretic concepts used and a process that leads to an automated TIN generation. A simple example is provided to illustrate the technique.

Keywords

Bayesian Network Mapping Rule Subject Matter Expert Cultural Behavior Peace Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wagenhals, L. W. (2000) Course of Action Development and Evaluation Using Discrete Event System Models of Influence Nets. PhD Dissertation, George Mason University, Fairfax, VA.Google Scholar
  2. 2.
    Wagenhals, L. W. & Levis, A. H. (2000) Course of Action Development and Evaluation. In: Proceedings of the 2000 Command and Control Research and Technology Symposium.Google Scholar
  3. 3.
    Wagenhals, L. W. & Levis, A. H. (2002) Modeling Support of Effects-Based Operations in War Games. In: Proc. 2002 Command and Control Research and Technology Symposium, Monterey, CA.Google Scholar
  4. 4.
    Haider, S. & Zaidi, A. K. (2004) Transforming Timed Influence Nets into Time Sliced Bayesian Networks. In: Proceedings of Command and Control Research and Technology Symposium.Google Scholar
  5. 5.
    Haider, S. & Levis, A. H. (2005) Dynamic Influence Nets: An Extension of Timed Influence Nets for Modeling Dynamic Uncertain Situations. In: Proc. 10th International Command and Control Research and Technology Symposium, Washington DC.Google Scholar
  6. 6.
    Haider, S., Zaidi, A. K. & Levis, A. H. (2004) A Heuristic Approach for Best Set of Actions Determination in Influence Nets. In: Proc. IEEE International Conference on Information Reuse and Integration, Las Vegas.Google Scholar
  7. 7.
    Hudson, L. D., Ware, B. S., Mahoney, S. M. & Laskey, K. B. (2001) An Application of Bayesian Networks to Anti-Terrorism Risk Management for Military Planners. Department of Systems Engineering and Operations Research, George Mason University.Google Scholar
  8. 8.
    SIAM: Influence Net modeler (SIAC), http://www.inet.saic.com/inet-public/siam.htm.
  9. 9.
    9. Douglas, B. L. (1995). CYC: A Large-Scale Investment in Knowledge Infrastructure. Commun. ACM 3833–38.Google Scholar
  10. 10.
    Mueller, E. T. (1997). Natural Language Processing with ThoughtTreasure: Signiform.Google Scholar
  11. 11.
    11. George, A. M. (1995). WordNet: A Lexical Database for English. Commun. ACM 3839–41.Google Scholar
  12. 12.
    Ding, L., Finin, T., Joshi, A., Pan, R., Cost, R. S., Peng, Y., Reddivari, P., Doshi, V. C. & Sachs, J. (2004) Swoogle: A Search and Metadata Engine for the Semantic Web. In: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management. ACM Press.Google Scholar
  13. 13.
    Grosso, W. E., Eriksson, H., Fergerson, R. W., Gennari, J. H., Tu, S. W. & Musen, M. A. (1999) Knowledge Modeling at the Millennium (the design and evolution of protege-2000). In: Proceedings of the Twelfth Workshop on Knowledge Acquisition, Modeling and Management, pp. 16–21.Google Scholar
  14. 14.
    Kalyanpur, A., Parsia, B., Sirin, E., Grau, B. C. & Hendler, J. (2006). Swoop: A Web Ontology Editing Browser. Web Semantics: Science, Services and Agents on the World Wide Web 4: 144–153.Google Scholar
  15. 15.
    15. Evren, S.,Bijan, P., Bernardo Cuenca, G., Aditya, K. & Yarden, K. (2007). Pellet: A practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 551–53.CrossRefGoogle Scholar
  16. 16.
    Mcguinness, D. L. & van Harmelen, F. (2004) OWL Web Ontology Language Overview. World Wide Web Consortium.Google Scholar
  17. 17.
    Beckett, D. (2004) RDF/XML Syntax Specification (Revised).Google Scholar
  18. 18.
    Pythia: Timed Influence Net Modeler, http://sysarch.gmu.edu/main/software/. SAL-GMU.
  19. 19.
    (2008) SPARQL Query Language for RDF.Google Scholar
  20. 20.
    Wagenhals, L. W. & Levis, A. H. (2007) Course of Action Analysis in a Cultural Landscape Using Influence Nets. Computational Intelligence in Security and Defense Applications, 2007. CISDA 2007. IEEE Symposium on pp. 116–123)Google Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Faisal Mansoor
    • 1
  • Abbas K. Zaidi
    • 1
  • Lee Wagenhals
    • 1
  • Alexander H. Levis
    • 1
  1. 1.George Mason UniversityFairfax

Personalised recommendations