Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis

arXiv:2205.14229 · cs.AI · Submitted 2022-05-27 · Read on arXiv

cs.AI

Submitted: 2022-05-27

Updated: 2022-10-17

Journal ref: Advances in Neural Information Processing Systems, volume 35 (2022) 4843--4856

DOI: 10.52202/068431-0350

Project page: https://dselsam.github.io/IMO-GC-battle-of-ideas

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 21× in this paper · explained in 4 episodes
• Transformer — 16× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Dynamic Graph Transformer — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Hybrid Approach — 2× in this paper · explained in 4 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• alpha — 2× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Markov Decision Process — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• SMT solver — 1× in this paper · explained in 2 episodes

Sources

• Learning to Reason in Large Theories without Imitation
• Accelerating Self-Play Learning in Go
• Universal Policies for Software-Defined MDPs
• Learning to Prove from Synthetic Theorems
• Generative Language Modeling for Automated Theorem Proving

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