Abstract
The last several decades have seen an unprecedented increase in the types and amount of information pertaining to the military environment. For the military commander and his staff, separating the important information from the routine has become a primary challenge in calculating the Value of Information (VOI). Wrought with uncertainty and contradiction, new methodologies are required to confront this significant issue. This paper presents an approach for calculating the VOI in complex military environments using fuzzy logic as a method for managing uncertain and imprecise information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alberts, D.S., Garstka, J.J., Hayes, R.E., Signori, D.T.: Understanding Information Age Warfare. CCRP, Washington (2001)
Flynn, M.T., et al.: Fixing Intel: A Blueprint for Making Intelligence relevant in Afghanistan, US Army (January 5, 2010)
James, J.: “Military Data”, presentation, Network Science Center, West Point (October 2010)
Ahituv, N.: Assessing the value of information: Problems and approaches. Paper presented at the Proceedings of the Tenth International Conference on Information Systems, Boston, MA (1989)
Rafaeli, S., Raban, D.R.: Experimental investigation of the subjective value of information in trading. Journal of the Association for Information Systems 4(5), 119–139 (2003)
Anonymous, US Army Field Manual (FM) 3-0, Operations, US Army (June 2001)
Wilkins, D.E., et al.: Interactive Execution Monitoring of Agent Teams. Journal of Artificial Intelligence Research 18 (March 2003)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics 3, 28–44 (1973)
Zadeh, L.A.: A theory of approximate reasoning. In: Yager, R., Orchinnikov, S., Tong, R., Nguyen, H. (eds.) Fuzzy Sets and Applications, pp. 367–412. John Wiley & Sons, New York (1987)
Zadeh, L.A.: The Concept of a Linguistic Variable - I. Information Sciences 8, 199–249 (1975)
Agrawal, P., Sarma, A., Ullman, J., Widom, J.: Foundations of Uncertain-Data Integration. In: Proceedings of the VLDB Endowment, vol. 3(1-2), pp. 1080–1090 (September 2010)
Magnani, M., Montesi, D.: A Survey on Uncertainty Management in Data Integration. Journal of Data and Information Quality 2(1), 5:1–5:33 (2010)
Helfert, M., Foley, O.: A Context Aware Information Quality Framework. In: Proceedings of the Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology, pp. 187–193 (November 2009)
Yu, B., Kallurkar, S., Vaidyanathan, G., Steiner, D.: Managing the Pedigree and Quality of Information in Dynamic Information Sharing Environments. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1248–1250 (May 2007)
Parsons, S.: Current Approaches to Handling Imperfect Information in Data and Knowledge Bases. IEEE Transactions on Knowledge and Data Engineering 8(3), 353–372 (1996)
Wang, R.Y., Strong, D.: Beyond Accuracy. What Data Quality Means to Data Consumers. Journal of Management Information Systems 12(4), 5–34 (1996)
Yen, J., Langari, R.: Fuzzy Logic: Intelligence, Control, and Information. Prentice Hall, Upper Saddle River (1999)
Liang, Y.: An Approximate Reasoning Model for Situation and Threat Assessment. In: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 246–250 (November 2007)
Vincenti, G., Hammell II, R.J., Trajkovski, G.: Scouting for Imprecise Temporal Associations to Support Effectiveness of Drugs During Clinical Trials. In: Proceedings of the Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS 2005), Ann Arbor, MI (June 22-25, 2005)
Barnes, A., Hammell II, R.J.: Employing Intelligent Decision Systems to Aid in Information Technology Project Status Decisions. In: Nag, B. (ed.) Intelligent Systems in Operations: Models, Methods, and Applications, pp. 1–26. IGI Global, Hershey (2010)
McQuighan, J., Hammell II, R.J.: Computational Intelligence for Project Scope. In: Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference, Cincinnati, OH, April 16-17, pp. 47–53 (2011)
Tolosa, J., Guadarrama, S.: Collecting Fuzzy Perceptions from Non-expert Users. In: Proceedings of the 19th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010), Barcelona, (Spain), pp. 1–8 (July 2010)
Cerruti, M., Das, S., Ashenfelter, A., Raven, G., Brooks, R., Sudit, M., Chen, G., Wright, E.: Pedigree Information for Enhanced Situation and Threat Assessment. In: Proceedings of the Ninth International Conference on Information Fusion, pp. 1–8 (July 2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hanratty, T., Hammell, R.J., Heilman, E. (2011). A Fuzzy-Based Approach to the Value of Information in Complex Military Environments. In: Benferhat, S., Grant, J. (eds) Scalable Uncertainty Management. SUM 2011. Lecture Notes in Computer Science(), vol 6929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23963-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-23963-2_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23962-5
Online ISBN: 978-3-642-23963-2
eBook Packages: Computer ScienceComputer Science (R0)