Linear resolution for consequence finding
Artificial Intelligence
Logical features of Horn Clauses
Handbook of logic in artificial intelligence and logic programming (vol. 1)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Horn approximations of empirical data
Artificial Intelligence
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
A General Framework for Knowledge Compilation
PDK '91 Proceedings of the International Workshop on Processing Declarative Knowledge
Completeness theorems for semantic resolution in consequence-finding
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
An analysis of approximate knowledge compilation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Knowledge compilation using theory prime implicates
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A new method for consequence finding and compilation in restricted languages
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Knowledge Compilation Using the Extension Rule
Journal of Automated Reasoning
First order LUB approximations: characterization and algorithms
Artificial Intelligence - Special volume on reformulation
A survey on knowledge compilation
AI Communications
An algorithm for computing theory prime implicates in first order logic
International Journal of Information and Communication Technology
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
First order LUB approximations: characterization and algorithms
Artificial Intelligence - Special volume on reformulation
Belief revision within fragments of propositional logic
Journal of Computer and System Sciences
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Knowledge compilation procedures make a knowledge base more explicit so as make inference with respect to the compiled knowledge base tractable or at least more efficient. Most work to date in this area has been restricted to the propositional case, despite the importance of first order theories for expressing knowledge concisely. Focusing on (LUB) approximate compilation (Selman and Kautz 1991), our contribution is twofold: • We present a new ground algorithm for approximate compilation which can produce exponential savings with respect to the previously known algorithm (Selman and Kautz 1991). • We show that both ground algorithms can be lifted to the first order case preserving their correctness for approximate compilation.