Saturation, nonmonotonic reasoning and the closed-world assumption
Artificial Intelligence
Artificial Intelligence
Causes for events: their computation and applications
Proc. of the 8th international conference on Automated deduction
An algorithm to compute circumscription
Artificial Intelligence
A circumscriptive theorem prover
Artificial Intelligence
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
An incremental method for generating prime implicants/implicates
Journal of Symbolic Computation
A fixpoint semantics for disjunctive logic programs
Journal of Logic Programming
On theorem provers for circumscription
Proceedings of the eighth biennial conference of the Canadian Society for Computational Studies of Intelligence on CSCSI-90
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Two Results on Ordering for Resolution with Merging and Linear Format
Journal of the ACM (JACM)
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
An Abductive Procedure for the CMS/ATMS
ECAI '90 Workshop on Truth Maintenance Systems
Rationale and Methods for Abductice Reasoning in Natural-Language Interpretation
Proceedings of the International Symposium on Natural Language and Logic
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
Automated theorem proving: A logical basis (Fundamental studies in computer science)
Automated theorem proving: A logical basis (Fundamental studies in computer science)
How to Produce Information About a Given Entity Using Automated Deduction Methods
Electronic Notes in Theoretical Computer Science (ENTCS)
Non-conservative extension of a peer in a P2P inference system
AI Communications
Probabilistic assumption-based reasoning
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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Since linear resolution with clause ordering is incomplete for consequence-finding, it has been used mainly for proof-finding. In this paper, we re-evaluate consequence-finding. Firstly, consequence-finding is generalized to the problem in which only interesting clauses having a certain property (called characteristic clauses) should be found. Then, we show how adding a skip rule to ordered linear resolution makes it complete for consequence-finding in this general sense. Compared with set-of-support resolution, the proposed method generates fewer clauses to find such a subset of consequences. In the propositional case, this is an elegant tool for computing the prime implicants/implicates. The importance of the results lies in their applicability to a wide class of AI problems including procedures for nonmonotonic and abductive reasoning and truth maintenance systems.