Empirical decision model learning
作者：
摘要
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.
论文关键词：Combinatorial optimization,Machine learning,Complex systems,Local search,Constraint programming,Mixed integer non-linear programming,SAT modulo theories,Artificial neural networks,Decision trees
论文评审过程：Revised 21 December 2015, Accepted 10 January 2016, Available online 13 January 2016, Version of Record 9 February 2017.
论文官网地址：https://doi.org/10.1016/j.artint.2016.01.005
