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deepmind.google·2h ago
AI Research
arXiv CS.AI·3d ago·5 min read
Learning Size Independent Constraint Models from Input Data and Positive and Negative Example Solutions
TL;DR
A new Constraint Acquisition Tool generates size-independent constraint models from input data and example solutions, producing MiniZinc code. Evaluated on PTHG21 Challenge problems, it demonstrates effective generalization for unseen data.
✦ Why It Matters
Implement this tool to automate the generation of constraint models for your projects today.
Key Takeaways
- Utilize the Constraint Acquisition Tool for efficient model generation.
- Leverage MiniZinc for implementing generated constraint models.
- Test the tool on diverse problem instances for validation.
- Explore generalization capabilities for unseen data applications.
How It Works
The tool analyzes input data and example solutions to identify common constraint patterns. It first creates size-specific models based on these patterns and then generalizes them into a size-independent format.
This allows the generated models to be applicable to new data without requiring size adjustments.