Bayesian ranking for strategy scheduling in automated theorem provers

DOI10.1007/978-3-031-10769-6_33OpenAlexW4289104048MaRDI QIDQ2104545

Sean B. Holden, Chaitanya Mangla, Lawrence Charles Paulson

Publication date: 7 December 2022

Full work available at URL: https://doi.org/10.1007/978-3-031-10769-6_33

zbMATH Keywords

automated theorem provingBayesian machine learningstrategy scheduling

Mathematics Subject Classification ID

Theorem proving (automated and interactive theorem provers, deduction, resolution, etc.) (68V15)

Uses Software

• Matlab
• TPTP
• leanCoP
• ileanCoP
• FaCT++
• E Theorem Prover
• PMTK
• kepler98
• MachSMT
• MedleySolver

Cites Work

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• Detecting Novel Associations in Large Data Sets
• Optimal scaling for various Metropolis-Hastings algorithms.
• Dyad ranking using Plackett-Luce models based on joint feature representations
• The TPTP problem library and associated infrastructure. From CNF to TH0, TPTP v6.4.0
• Bayesian learning for neural networks
• MedleySolver: online SMT algorithm selection
• Machine learning for first-order theorem proving
• leanCoP 2.0 and ileanCoP 1.2: High Performance Lean Theorem Proving in Classical and Intuitionistic Logic (System Descriptions)
• 10.1162/15324430152748236
• A FORMAL PROOF OF THE KEPLER CONJECTURE
• Bayesian Reasoning and Machine Learning
• Automated Reasoning with Analytic Tableaux and Related Methods

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