Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
Instance-Based Learning Algorithms
Machine Learning
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
Patching Proofs for Reuse (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Flexible Re-enactment of Proofs
EPIA '97 Proceedings of the 8th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Applying Case-Based Reasoning to Automated Deduction
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
DISCOUNT: A SYstem for Distributed Equational Deduction
RTA '95 Proceedings of the 6th International Conference on Rewriting Techniques and Applications
Analogical Reasoning and Proof Discovery
Proceedings of the 9th International Conference on Automated Deduction
The Use of Explicit Plans to Guide Inductive Proofs
Proceedings of the 9th International Conference on Automated Deduction
Automatic Acquisition of Search Guiding Heuristics
Proceedings of the 10th International Conference on Automated Deduction
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Learning Domain Knowledge to Improve Theorem Proving
CADE-13 Proceedings of the 13th International Conference on Automated Deduction: Automated Deduction
CODE: A Powerful Prover for Problems of Condensed Detachment
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
High performance ATP systems by combining several AI methods
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
A model of analogy-driven proof-plan construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Feature‐based learning of search‐guiding heuristics for theorem proving
AI Communications
Machine learning approach to enhance the design of automated theorem provers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Automated reasoning or theorem proving essentially amounts to solving search problems. Despite significant progress in recent years theorem provers still have many shortcomings. The use of machine-learning techniques is acknowledged as promising, but difficult to apply in the area of theorem proving. We propose here to learn search-guiding heuristics by employing features in a simple, yet effective manner. Features are used to adapt a heuristic to a solved source problem. The adapted heuristic can then be utilized profitably for solving related target problems. Experiments have demonstrated that the approach not only allows for significant speed-ups, but also makes it possible to prove problems that were out of reach before.