Home // International Journal On Advances in Systems and Measurements, volume 17, numbers 3 and 4, 2024 // View article

Symbolic Unfolding versus Tuning of Similarity-based Fuzzy Logic Programs

Authors:
Gines Moreno
José Antonio Riaza

Keywords: Fuzzy Logic Programming; Similarity; Symbolic Unfolding; Tuning

Abstract:
FASILL introduces “Fuzzy Aggregators and Similarity Into a Logic Language”. In its symbolic extension, called sFASILL, some truth degrees, similarity annotations and fuzzy connectives can be left unknown, so that the user can easily figure out the impact of their possible values at execution time. In this paper, we firstly adapt to this last setting a similarity-based, symbolic variant of unfolding rule (very well known in most declarative frameworks), which is based on the application of computational steps on the bodies of program rules for improving efficiency. Next, we combine it with previous tuning techniques intended to transform a symbolic sFASILL program into the concrete customized FASILL one that best satisfies the user’s preferences. The improved methods have been implemented in a freely available online tool, which has served us to develop several experiments and benchmarks evidencing the good performance of the resulting system. To the best of our knowledge, our analysis is the first one combining unfolding and tuning techniques in a fully integrated fuzzy logic programming setting.

Pages: 189 to 200

Copyright: Copyright (c) to authors, 2024. Used with permission.

Publication date: December 30, 2024

Published in: journal

ISSN: 1942-261x