A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Abstract

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi∼Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

Tuning Fuzzy Logic Programs with Symbolic Execution

“…Currently, the system can be used to compile MALP programs to standard Prolog code, draw derivation trees, generate declarative traces, and execute MALP programs, and it is ready for being extended with powerful transformation and optimization techniques [11,12,13]. Our last update described in [14,15], allows the system to cope with similarity relations cohabiting with lattices of truth degrees, since this feature is an interesting topic for being embedded into the new tuning technique in the near future. Another interesting direction for further research, consists in combining our approach with recent fuzzy variants of SAT/SMT techniques.…”

“…Most of these systems implement (extended versions of) the resolution principle introduced by Lee [19], such as Elf-Prolog [10], F-Prolog [20], generalized annotated logic programming [17], Fril [4], MALP [24], R-fuzzy [9], the QLP scheme of [31] and the many-valued logic programming language of [36,34]. There exists also a family of fuzzy languages based on sophisticated unification methods [33] which cope with similarity/proximity relations, as occurs with Likelog [3], SQLP [8], Bousi∼Prolog [16,32] and FASILL [14,15]. Some related approaches based on probabilistic logic programming can be found, e.g., in [29,7].…”

Розробка Скриптової Мови Запису Правил В Експертній Системі На Основі Нечіткої Логіки

“…Правила в програмі FASILL мають ту ж роль, що й Prolog. Імплементовано у системі FLOPER, в свою чергу FLOPER реалізовано з використанням Sicstus Prolog версії 3.12.5, система доступна онлайн і використовується для різноманітних досліджень [9].…”

On the Incorporation of Interval-Valued Fuzzy Sets into the Bousi-Prolog System: Declarative Semantics, Implementation and Applications

“…Inside the former and current frameworks of fuzzy logic programming [5,6,7,8,9,10], we argue that lexical reasoning might be an appropriate way for tackling this challenge, because of this type of knowledge is usually expressed linguistically. However, from a computational point of view, this source of information involves vagueness and uncertainty and, consequently, it must be specifically addressed.…”