Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A fuzzy Prolog database system
A fuzzy Prolog database system
Partial evaluation and automatic program generation
Partial evaluation and automatic program generation
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Multi-adjoint Logic Programming with Continuous Semantics
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Efficient Reductants Calculi using Partial Evaluation Techniques with Thresholding
Electronic Notes in Theoretical Computer Science (ENTCS)
Prolog-ELF incorporating fuzzy logic
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
On fuzzy unfolding: A multi-adjoint approach
Fuzzy Sets and Systems
Programming with Fuzzy Logic Rules by Using the FLOPER Tool
RuleML '08 Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web
Modeling Interpretive Steps in Fuzzy Logic Computations
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
A practical management of fuzzy truth-degrees using FLOPER
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Declarative traces into fuzzy computed answers
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
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Multi-adjoint logic programming represents an extremely flexible attempt for introducing fuzzy logic into logic programming (LP). In this setting, the execution of a goal w.r.t. a given program is done in two separate phases. During the operational one, admissible stepsare systematically applied in a similar way to classical resolution steps in pure LP, thus returning an expression where all atoms have been exploited. This last expression is then interpreted under a given lattice during the so called interpretive phase. In declarative programming, it is usual to estimate the computational effort needed to execute a goal by simply counting the number of steps required to reach their solutions. In this paper, we show that although this method seems to be acceptable during the operational phase, it becomes inappropriate when considering the interpretive one. Moreover, we propose a more refined (interpretive) cost measure which fairly models in a much more realistic way the computational (special interpretive) a given goal.