DocumentCode :
2134551
Title :
Bayesian estimation vs fuzzy logic for heuristic reasoning
Author :
De Mathelin, Michel ; Perneel, Christiaan ; Acheroy, Marc
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
1993
fDate :
1993
Firstpage :
944
Abstract :
Bayesian estimation theory and fuzzy logic are used to derive knowledge combination rules for heuristic search algorithms. Such algorithms are typically used with expert systems. Heuristics are used to select candidate solutions or partial solutions of a complex problem whose solution space is too large to be fully explored. The information coming from the various heuristics and from observations made during the search must be combined. Combination and decision rules are first derived based on a probabilistic approach. Then, a fuzzy logic approach is followed and compared with the first approach
Keywords :
Bayes methods; fuzzy logic; heuristic programming; inference mechanisms; search problems; Bayesian estimation; candidate solutions; expert systems; fuzzy logic; heuristic reasoning; knowledge combination rules; partial solutions; probabilistic approach; solution space; Bayesian methods; Buildings; Estimation theory; Expert systems; Fuzzy logic; Heuristic algorithms; Military computing; Search methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 1993., Second IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0614-7
Type :
conf
DOI :
10.1109/FUZZY.1993.327387
Filename :
327387
Link To Document :
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