Constraint acquisition tool builds reusable programs from example solutions
Learning Size Independent Constraint Models from Input Data and Positive and Negative Example Solutions
Artificial Intelligence
Summary
Building computer programs that solve problems with specific rules can be hard, especially if the program needs to work for different problem sizes. This paper presents a tool that learns rules from examples of what works and what doesn’t. It then creates programs that are not tied to any particular problem size and can handle new inputs. The learned programs can be used directly with MiniZinc, a popular tool for solving such rule-based problems. The authors tested the tool and showed it can produce useful, flexible models.
What this means in practice
- •For software developers: Automatically generate reusable constraint programs from example problem solutions to handle varying input sizes efficiently.
- •For operations research teams: Use the tool to create flexible optimization models from example data for scheduling and resource allocation problems of different scales.
Authors
Helmut Simonis
Abstract
In this paper we describe a Constraint Acquisition Tool that generates size independent constraint programs from input data and positive and negative example solutions of multiple problem instances. The system finds size specific models based on typical constraint patterns over global and binary constraints, then generalizes the models to a size independent model that can be run on unseen input data. The system produces MiniZinc code as well as a textual description of the model. We evaluate the tool on the example problems of the PTHG21 Challenge and show the feasibility of the approach.