DocumentCode :
318086
Title :
High-level decisions from low-level data
Author :
Beers, Suzanne M.
Author_Institution :
Air Force Oper. Test & Evaluation Center, Kirtland AFB, NM, USA
Volume :
2
fYear :
1997
fDate :
12-15 Oct 1997
Firstpage :
1948
Abstract :
Decision-makers long for information that will make their decision processes easy and accurate. A methodology is proposed which begins with low information-content data, such as that derived from system testing, and aggregates/synthesizes the information to a higher information-content level, where it is meaningful to the decision-maker. The entire methodology, termed the intelligent hierarchical decision architecture, is composed of four stages which takes low level test data gathered at the functional performance information level as input and the final output is a probabilistic bound on the system performance at the operational task-accomplishment information level
Keywords :
associative processing; case-based reasoning; cognitive systems; content-addressable storage; decision support systems; fuzzy set theory; fuzzy systems; clustering; evidential reasoning; fuzzy associative memory; fuzzy cognitive map; fuzzy membership function; intelligent hierarchical decision architecture; low level test data; probabilistic bound; task-accomplishment information level; Associative memory; Clustering methods; Fuzzy cognitive maps; Fuzzy control; Fuzzy sets; Laboratories; Optimization methods; Performance evaluation; System performance; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1062-922X
Print_ISBN :
0-7803-4053-1
Type :
conf
DOI :
10.1109/ICSMC.1997.638360
Filename :
638360
Link To Document :
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