DecisionCAMP Session “Learning Executable Constraint Models” on Dec 15, 2021

Date: Wed, December 15, 2021 at 12:00pm EST (New York Time) Title: “Learning Executable Constraint Models from Positive and Negative Examples” Presenter: Helmut Simonis, Insight Research Centre for Data Analytics in Ireland. Abstract: We discuss how to learn constraint models for combinatorial problems from positive and negative example solutions.  The constraints learned are either structural, coming from the perceived problem structure of the model, or based on input data which provide parameters and index sets. The learning process uses information about global constraints from the Global Constraint Catalogue, and defines a set of constraint pattern to discover conjunctions of similar constraints over common data structures. The CAT tool generates constraint models in MiniZinc, which can then be run by a variety of back-end solvers, it also produces a human readable description of the constraint model which allows a user to understand the model generated. It was tested on the benchmark problems of the PTHG21 Challenge. Watch recording: Please fill in the following fields to register for this session:

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