CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
This addresses the problem of accessibility for non-experts in combinatorial problem-solving using Constraint Programming, though it is incremental as it builds on existing models rather than generating new ones.
The paper tackles the difficulty for non-experts in using Constraint Programming due to complex modeling languages by proposing CP-Model-Zoo, a tutoring system that retrieves expert-written models from a database based on natural language descriptions, achieving excellent accuracy in experiments.
Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We propose a tutoring system called CP-Model-Zoo, exploiting expert-written models accumulated through the years. CP-Model-Zoo retrieves the closest source code model from a database based on a user's natural language description of a combinatorial problem. It ensures that expert-validated models are presented to the user while eliminating the need for human data labeling. Our experiments show excellent accuracy in retrieving the correct model based on a user-input description of a problem simulated with different levels of expertise.