Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results
Abstract
The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. Associated with each predicted risk reasoning result is a quantifiable amount of information uncertainty which can be measured and used to compare the results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. The measured quantities of conflict and non-specificity can ultimately be used to compare qualitative reasoning results which are important in triage studies and ultimately resource allocation. Straight forward extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.
- Authors:
-
- Los Alamos National Laboratory
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 992615
- Report Number(s):
- LA-UR-09-05752; LA-UR-09-5752
TRN: US201022%%535
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Conference
- Resource Relation:
- Conference: ICAART 2010 ; January 20, 2010 ; Valencia, Spain
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; 97 MATHEMATICS AND COMPUTING; ARTIFICIAL INTELLIGENCE; ENTROPY; RISK ASSESSMENT; SECURITY; SIMULATION
Citation Formats
Chavez, Gregory M, Key, Brian P, Zerkle, David K, and Shevitz, Daniel W. Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results. United States: N. p., 2009.
Web.
Chavez, Gregory M, Key, Brian P, Zerkle, David K, & Shevitz, Daniel W. Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results. United States.
Chavez, Gregory M, Key, Brian P, Zerkle, David K, and Shevitz, Daniel W. 2009.
"Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results". United States. https://www.osti.gov/servlets/purl/992615.
@article{osti_992615,
title = {Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results},
author = {Chavez, Gregory M and Key, Brian P and Zerkle, David K and Shevitz, Daniel W},
abstractNote = {The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. Associated with each predicted risk reasoning result is a quantifiable amount of information uncertainty which can be measured and used to compare the results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. The measured quantities of conflict and non-specificity can ultimately be used to compare qualitative reasoning results which are important in triage studies and ultimately resource allocation. Straight forward extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.},
doi = {},
url = {https://www.osti.gov/biblio/992615},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2009},
month = {Thu Jan 01 00:00:00 EST 2009}
}