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Found 7 papers, 4 papers with code
Fast Converging Anytime Model Counting
Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification.
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice.
CCDD: A Tractable Representation for Model Counting and Uniform Sampling
Knowledge compilation concerns with the compilation of representation languages to target languages supporting a wide range of tractable operations arising from diverse areas of computer science.
Benchmarking Symbolic Execution Using Constraint Problems -- Initial Results
We transform CSP benchmarks into C programs suitable for testing the reasoning capabilities of symbolic execution tools.
Software Engineering Logic in Computer Science I.2.0; I.2.1; I.2.3; I.2.4; I.2.8; I.2.11; D.2
Learning Robust Search Strategies Using a Bandit-Based Approach
However, choosing or designing a good search heuristic is non-trivial and is often a manual process.
Correlation Heuristics for Constraint Programming
We evaluate our correlation heuristics with well known heuristics, namely, dom/wdeg, impact-based search and activity-based search.
Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores
Stochastic cracking also uses each query as a hint on how to reorganize data, but not blindly so; it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decision-making.
