Abstract
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 steps are 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.
This work has been partially supported by the EU, under FEDER, and the Spanish Science and Education Ministry (MEC) under grant TIN 2004-07943-C04-03.
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Julián, P., Moreno, G., Penabad, J. (2007). Measuring the Interpretive Cost in Fuzzy Logic Computations. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_4
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DOI: https://doi.org/10.1007/978-3-540-73400-0_4
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