Causes for events: their computation and applications
Proc. of the 8th international conference on Automated deduction
Exploiting constraints in design synthesis
Exploiting constraints in design synthesis
On the relation between default and autoepistemic logic
Artificial Intelligence
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
Logic programs with classical negation
Logic programming
Conditional entailment: bridging two approaches to default reasoning
Artificial Intelligence
Unfounded sets and well-founded semantics for general logic programs
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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We present an abductive semantics for general propositional logic programs which defines the meaning of a logic program in terms of its extensions. This approach extends the stable model semantics for normal logic programs in a natural way. The new semantics is equivalent to stable semantics for a logic program P whenever P is normal and has a stable model. The abductive semantics can also be applied to generalize default logic and autoepistemic logic in a like manner. Our approach is based on an idea recently proposed by Konolige for causal reasoning. Instead of maximizing the set of hypotheses alone we maximize the union of the hypotheses, along with possible hypotheses that are excused or refuted by the theory.