Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 24·5 min read
Streamliners for Answer Set Programming
TL;DR
Streamliner constraints enhance Answer Set Programming (ASP) by using Large Language Models (LLMs) to generate constraints that reduce the search space, achieving speedups of 4 to 5 times on benchmark problems.
✦ Why It Matters
Consider integrating LLM-generated streamliner constraints into your ASP projects for significant performance gains.
Key Takeaways
- Explore using LLMs to generate constraints for your ASP applications.
- Evaluate the performance of generated streamliners against your existing encodings.
- Test the virtual best encoding approach on your combinatorial problems.
- Consider the benchmarks used in this study for comparative analysis.
How It Works
The process begins with an ASP encoding and a few small training instances. Multiple LLMs are prompted to generate candidate constraints, which are then filtered based on their impact on syntax and performance.
The surviving constraints are combined with the original encoding to form a virtual best encoding (VBE), which selects the most efficient variant for each problem instance.