Hierarchical knowledge bases and efficient disjunctive reasoning
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Tractable default reasoning
Realization of a geometry-theorem proving machine
Computers & thought
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Solving Time-Dependent Planning Problems
Solving Time-Dependent Planning Problems
A completeness theorem and a computer program for finding theorems derivable from given axioms
A completeness theorem and a computer program for finding theorems derivable from given axioms
Reflection and action under scarce resources: theoretical principles and empirical study
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Using model theory to specify AI programs
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Translating owl and semantic web rules into prolog: Moving toward description logic programs
Theory and Practice of Logic Programming
Resolution-Based approximate reasoning for OWL DL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Deductive inference for the interiors and exteriors of horn theories
ACM Transactions on Computational Logic (TOCL)
Approximate Inference In Default Logic And Circumscription
Fundamenta Informaticae
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We present a new approach to developing fast and efficient knowledge representation systems. Previous approaches to the problem of tractable inference have used restricted languages or incomplete inference mechanisms -- problems include lack of expressive power, lack of inferential power, and/or lack of a formal characterization of what can and cannot be inferred. To overcome these disadvantages, we introduce a knowledge compilation method. We allow the user to enter statements in a general, unrestricted representation language, which the system compiles into a restricted language that allows for efficient inference. Since an exact translation into a tractable form is often impossible, the system searches for the best approximation of the original information. We will describe how the approximation can be used to speed up inference without giving up correctness or completeness. We illustrate our method by studying the approximation of logical theories by Horn theories. Following the formal definition of Horn approximation, we present "anytime" algorithms for generating such approximations. We subsequently discuss extensions to other useful classes of approximations.