Automatic synthesis of constraints from examples using mixed integer linear programming

doi:10.1016/j.ejor.2017.02.034

Automatic synthesis of constraints from examples using mixed integer linear programming

2017-09-01

36

PRE

AI

DOI：10.1016/j.ejor.2017.02.034

原文链接

原文求助

摘要

En 中文

Constraints form an essential part of most practical search and optimization problems, and are usually assumed to be given. However, there are plausible real-world scenarios in which constraints are not known or can be only approximated, for instance when the process in question is complex and/or noisy. To address such problems, we propose a method that synthesizes constrains from examples of feasible and infeasible solutions. The method can produce linear, quadratic and trigonometric constraints that are guaranteed to separate the feasible and infeasible regions and minimize the number of terms involved. The synthesized constraints are represented symbolically and can be used to simulate, predict or optimize the original process. We assess empirically several characteristics of the method on three benchmarks, in particular the fidelity and the complexity of the synthesized constraints with respect to the actual constraints. We also demonstrate its application to a real-world process of concrete manufacturing. Experiments demonstrate that the method is capable of producing human-readable constraints that reflect well the underlying process and can be used to simulate it. (C) 2017 Elsevier B.V. All rights reserved.

Keyword:

Artificial intelligence

Constraint acquisition

Constraint learning

Model induction

Model generation

Automatic synthesis of constraints from examples using mixed integer linear programming

摘要

AI总结

期刊

机构

引用论文