Bucket elimination: a unifying framework for probabilistic inference
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
On some tractable classes in deduction and abduction
Artificial Intelligence
Partial Instantiation Methods for Inference in First-Order Logic
Journal of Automated Reasoning
Tractable Classes for Directional Resolution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Knowledge Compilation Using the Extension Rule
Journal of Automated Reasoning
Extended asp tableaux and rule redundancy in normal logic programs1
Theory and Practice of Logic Programming
Counting models using extension rules
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Dynamic theorem proving algorithm for consistency-based diagnosis
Expert Systems with Applications: An International Journal
Improved propositional extension rule
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
An extension rule based first-order theorem prover
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
| Hi-index | 0.00 |
Methods based on resolution have been widely used for theorem proving since it was proposed. This paper presents a new method for theorem proving that uses the inverse of resolution and employs the inclusion–exclusion principle to circumvent the problem of space complexity. We prove its soundness and completeness. The concept of complementary factor is introduced to estimate its complexity. We also show that our method outperforms resolution-based methods in some cases. Thus it is potentially a complementary method to resolution-based methods.