Streamliners for Answer Set Programming
Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the StreamLLM approach, which uses Large Language Models (LLMs) to generate streamliners for Constraint Programming, to Answer Set Programming (ASP). Given an ASP encoding and a few small training instances, we prompt multiple LLMs to propose candidate constraints. Candidates that cause syntax errors, render satisfiable instances unsatisfiable, or degrade performance on all training instances are discarded. The surviving streamliners are evaluated together with the original encoding, and we report results for a virtual best encoding (VBE) that, for each instance...
| Authors | Florentina Voboril, Martin Gebser, Stefan Szeider et al. |
|---|---|
| Primary category | cs.LO |
| Published | 21 April 2026 |
| Last updated at source | 23 July 2026 |
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