DocumentCode
3116762
Title
Scheduling with uncertain resources: Learning to make reasonable assumptions
Author
Gardiner, Steven ; Fink, Eugene ; Carbonell, Jaime G.
Author_Institution
Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA
fYear
2008
fDate
12-15 Oct. 2008
Firstpage
2554
Lastpage
2559
Abstract
We consider the task of scheduling a conference based on incomplete information about resources and constraints, and describe a mechanism for the dynamic learning of related default assumptions, which enable the scheduling system to make reasonable guesses about missing data. We outline the representation of incomplete knowledge, describe the learning procedure, and demonstrate that the learned knowledge improves the scheduling results.
Keywords
knowledge representation; learning (artificial intelligence); scheduling; conference scheduling; default assumptions dynamic learning; incomplete knowledge representation; reasonable guess; uncertain resources; Computer science; Dynamic scheduling; Mechanical factors; Microphones; Processor scheduling; Project management; Radar; Software agents; Software development management; Uncertainty; Uncertainty; elicitation; optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on
Conference_Location
Singapore
ISSN
1062-922X
Print_ISBN
978-1-4244-2383-5
Electronic_ISBN
1062-922X
Type
conf
DOI
10.1109/ICSMC.2008.4811680
Filename
4811680
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