SNoW User Guide
leanCoP: lean connection-based theorem proving
Journal of Symbolic Computation - Special issue: First order theorem proving
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
MPTP 0.2: Design, Implementation, and Initial Experiments
Journal of Automated Reasoning
The design and implementation of VAMPIRE
AI Communications - CASC
AI Communications - CASC
Translating Higher-Order Clauses to First-Order Clauses
Journal of Automated Reasoning
IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance
IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
Restricting backtracking in connection calculi
AI Communications - Practical Aspects of Automated Reasoning
External sources of axioms in automated theorem proving
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Evaluation of automated theorem proving on the Mizar mathematical library
ICMS'10 Proceedings of the Third international congress conference on Mathematical software
Sine Qua non for large theory reasoning
CADE'11 Proceedings of the 23rd international conference on Automated deduction
Automated and human proofs in general mathematics: an initial comparison
LPAR'12 Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Overview and evaluation of premise selection techniques for large theory mathematics
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Automated reasoning service for HOL light
CICM'13 Proceedings of the 2013 international conference on Intelligent Computer Mathematics
Theorem proving in large formal mathematics as an emerging AI field
Automated Reasoning and Mathematics
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Probabilistic guidance based on learned knowledge is added to the connection tableau calculus and implemented on top of the lean-CoP theorem prover, linking it to an external advisor system. In the typical mathematical setting of solving many problems in a large complex theory, learning from successful solutions is then used for guiding theorem proving attempts in the spirit of the MaLARea system. While in MaLARea learning-based axiom selection is done outside unmodified theorem provers, in MaLeCoP the learning-based selection is done inside the prover, and the interaction between learning of knowledge and its application can be much finer. This brings interesting possibilities for further construction and training of self-learning AI mathematical experts on large mathematical libraries, some of which are discussed. The initial implementation is evaluated on the MPTP Challenge large theory benchmark.