SETHEO: a high-performance theorem prover
Journal of Automated Reasoning
A feature-based learning method for theorem proving
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficiency and Completeness of the Set of Support Strategy in Theorem Proving
Journal of the ACM (JACM)
Evaluating general purpose automated theorem proving systems
Artificial Intelligence
Journal of Automated Reasoning
Journal of Automated Reasoning
Smart Selective Competition Parallelism ATP
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Strategy Selection by Genetic Programming
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Homogeneous Sets of ATP Problems
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
System Description: Spass Version 1.0.0
CADE-16 Proceedings of the 16th International Conference on Automated Deduction: Automated Deduction
System Description: LEO - A Higher-Order Theorem Prover
CADE-15 Proceedings of the 15th International Conference on Automated Deduction: Automated Deduction
Octopus: Combining Learning and Parallel Search
Journal of Automated Reasoning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
AI Communications - CASC
AI Communications - CASC
AI Communications - CASC
iProver --- An Instantiation-Based Theorem Prover for First-Order Logic (System Description)
IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
High performance ATP systems by combining several AI methods
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
The CADE-22 automated theorem proving system competition - CASC-22
AI Communications
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Extracting knowledge from hard to prove First-order predicate calculus problems and their solutions may lead to improve the performance of automated theorem provers. In this work, we examine the association between the syntactic characteristics of the theorems and the techniques (inference methods and the control strategies) used by various ATPs (automated theorem provers) when attempted these problems. The aim is to determine the most suitable combination of inference methods and control strategies for classes of problems based on their syntactic characteristics. Data mining classification techniques are used to extract knowledge from ATPs attempts on these problems. Such knowledge identifies meta-strategies that are capable to enhance the performance of ATPs. Design improvements in software engineering often enhance the process of software creation, verification, and maintenance. This is one of the primary objectives of this work.