Empirical decision model learning

One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization....

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Bibliographic Details
Published in:	Artificial intelligence Vol. 244; pp. 343 - 367
Main Authors:	Lombardi, Michele, Milano, Michela, Bartolini, Andrea
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.03.2017 Elsevier Science Ltd
Subjects:	Accuracy Analytics Approximation Artificial neural networks Combinatorial analysis Combinatorial optimization Complex systems Computer simulation Constraint programming Decision support systems Decision trees Empirical analysis Encapsulation Linear programming Local search Machine learning Mathematical models Mixed integer Mixed integer non-linear programming Neural networks Nonlinear programming Optimization SAT modulo theories Simulators Solvers Workload SAT modulo theories Local search Mixed integer non-linear programming Constraint programming Machine learning Artificial neural networks Combinatorial optimization Decision trees Complex systems
ISSN:	0004-3702, 1872-7921
Online Access:	Get full text
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