Introduction to artificial intelligence
Introduction to artificial intelligence
Abduction versus closure in causal theories
Artificial Intelligence
Representing incomplete knowledge in abductive logic programming
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
On the equivalence between disjunctive and abductive logic programs
Proceedings of the eleventh international conference on Logic programming
The complexity of logic-based abduction
Journal of the ACM (JACM)
ACM Transactions on Database Systems (TODS)
Abduction from logic program: semantics and complexity
Theoretical Computer Science
On the complexity of unique solutions
Journal of the ACM (JACM)
Declarative problem-solving using the DLV system
Logic-based artificial intelligence
Enhancing Disjunctive Datalog by Constraints
IEEE Transactions on Knowledge and Data Engineering
Database Updates through Abduction
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Normality and faults in logic-based diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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Abduction, first proposed in the setting of classical logics, has been studied with growing interest in the logic programming area during the last years. In this paper we study abduction with penalization in logic programming. This form of abductive reasoning, which has not been previously analyzed in logic programming, turns out to represent several relevant problems, including optimization problems, very naturally. We define a formal model for abduction with penalization from logic programs, which extends the abductive framework proposed by Kakas and Mancarella. We show the high expressiveness of this formalism, by encoding a couple of relevant problems, including the well-know Traveling Salesman Problem from optimization theory, in this abductive framework. The resulting encodings are very simple and elegant. We analyze the complexity of the main decisional problems arising in this framework. An interesting result in this course is that "negation comes for free." Indeed, the addition of (even unstratified) negation does not cause any further increase to the complexity of the abductive reasoning tasks (which remains the same as for not-free programs).