Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A fuzzy Prolog database system
A fuzzy Prolog database system
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
Measuring the Interpretive Cost in Fuzzy Logic Computations
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Optimizing Fuzzy Logic Programs by Unfolding, Aggregation and Folding
Electronic Notes in Theoretical Computer Science (ENTCS)
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
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
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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Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming (LP). In this setting, the multi-adjoint logic approach represents an extremely flexible fuzzy language with a procedural principle structured in two separate phases. During the operational one, admissible steps are systematically applied in a similar way to classical resolution steps in 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. Whereas the operational phase has been successfully formalized in the past, more effort is needed to clarify the notion of interpretive step . In this paper we firstly introduce a refinement of this concept which fairly models at a very low level the computational behaviour of the interpretive phase. Then, we present a simple but powerful cost measure induced from such definition which helps to estimate the computational (interpretive) effort required to solve a goal. The resulting method is much more accurate and realistic than other simpler cost measures (like counting the number or the weights of interpretive steps) that we have proposed in the past for proving efficiency properties in program transformation tasks such as fold/unfold, partial evaluation, and so on.