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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
Extending SMT Solving with Non-Ground Clause Learning
TL;DR
A new calculus for SMT (Satisfiability Modulo Theories) solving incorporates non-ground clause learning, enabling more general conflict analysis and potentially shorter proofs. This approach enhances traditional ground reasoning methods by allowing solvers to learn from non-ground clauses, improving efficiency in solving complex problems.
✦ Why It Matters
Consider adopting non-ground clause learning in your SMT solving processes to enhance proof efficiency.
Key Takeaways
- Implement non-ground clause learning in your SMT solvers.
- Explore the integration of chronological backtracking for improved reasoning.
- Utilize the proposed calculus for more general learned clauses.
- Evaluate the performance of solvers using this new approach.
How It Works
The proposed calculus combines ground instantiations with CDCL(T)-style reasoning and non-ground conflict analysis. When a conflict is detected, the solver analyzes the original non-ground clauses instead of just the ground instances.
This allows for the generation of learned clauses that are more general, potentially leading to shorter proofs. The method also includes a strategy for ensuring that the learned clauses are non-redundant, enhancing the overall efficiency of the solving process.