In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and im- plementation for this extension is presented and formalised. We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning.
In the literature, other proposals that address our same goal can be found @cite_0 @cite_4 . One of the most relevant ones is Ciao-Prolog @cite_4 and, for that reason, we will analyse in detail the differences between it and Bousi-Prolog in order to clarify and reinforce the novelty of our proposal:
@cite_0 @cite_4
2001706342 2007452568
Abstract A logic programming system which uses a theory of intuitionistic fuzzy sets to model various forms of uncertainty is presented. To represent uncertainty of facts and rules, a pair of two different real numbers (degree of truth and degree of falsity) are associated. The problem of propagating uncertainty through logical inference and various models of interpretation are considered. The framework discussed allows knowledge representation and inference under uncertainty in the form of rules suitable for expert systems. We present a definition of a Fuzzy Prolog Language that models B([0,1])-valued Fuzzy Logic, and subsumes former approaches because it uses a truth value representation based on a union of sub-intervals on [0,1] and is defined using general operators that can model different logics. This extension to Prolog is implemented by interpreting fuzzy reasoning as a set of constraints that are propagated through the rules by means of aggregation operators. Declarative and procedural semantics for Fuzzy Logic programs are given and their equivalence is proven. In addition, we present the implementation of an interpreter for this conceived language using constraint logic programming over real numbers.