INTRODUCTION

Logic Programming is founded on the idea that logic, or at least substantial subsets of it, can be used as a programming language. Although logic programming has been used in a wide range of applications such as problem solving and knowledge representation, deductive database systems, expert systems or natural language processing, it has not direct tools to deal with the essential vagueness or uncertainty of some problems.

Fuzzy Logic Programming amalgamates fuzzy logic and pure logic programming, in order to provide these languages with the ability of dealing with uncertainty and approximated reasoning. One of the main advantages of this combination is the construction of programming languages that allow us to deal with imprecise information by using declarative techniques. 

Bousi~Prolog (BPL) is a fuzzy logic programming language that replaces the syntactic unification mechanism of classical SLD-resolution by a fuzzy unification algorithm. This algorithm provides a weak most general unifier as well as a numerical value, called the unification degree. Intuitively, the unification degree represents the truth degree associated with the (query) computed instance. Then, the core of BPL operational semantics is a fuzzy unification algorithm based on proximity relations (that is reflexive and symmetric, but not necessarily transitive, binary fuzzy relations on a set). Hence, generalizing the operational mechanism of Maria I. Sessa, based on similarity relations, and increasing the expressive power of the resulting language. Proximity relations allow us to model problems where the transitivity restriction of a similarity relation is an obstacle.

Also we use proximity relations to implement  fuzzy sets (linguistic variables),  obtaining an elegant, simple, natural and efficient fuzzy Prolog system based on weak unification.

Starting with version 3.1 of the BPL system, it integrates WordNet. New versions, such as version 3.3, has increased the connection with WordNet.

Since version 3.5 the BPL system incorporates “graded rules“, that is, fuzzy rules can be annotated with an approximation degree. Now, the degree of a computation has two sources: the approximation degree provided by the weak unification algorithm and the annotation of the rule used to perform the step. The BPL user can choose the t-norm used to combine these degrees among a wide set of t-norms: product, luka, drastic, nilpotent, and hamacher.

Version 3.6 has introduced the ability to work with Definite Clause Grammars (DCG) natively.  DCGs were initially a notation for writing production rules for defining grammars, but now they can be used to parse, generate and check lists. In addition, they form a comfortable interface to difference lists.

Bousi~Prolog and efficiency 
From the beginning we had a great concern with efficiency. For instance, key proceses for the weak unification algorithm such as the generation of closures or proximity blocks of fuzzy relations, have been moved and confined to the compilation phase, avoiding its computation at runtime.

Version 3.2 was a step ahead for efficiency by means of the implementation of novel filtering techniques that optimize the TPL code generated after the parsing and translating into Prolog phase. Also in version 3.6 we have developed new techniques that allow us to preserve the Prolog tail recursion optimization and even recover certain aspects of its indexing techniques in the framework of high-level implementation. Now, thanks to these optimization techniques, Bousi~Prolog can process larger programs.

Experiments confirm that Bousi~Prolog, equipped with these optimization techniques, is more than four times faster than other comparable systems when it comes to solving a problem that involves weak unification tasks.

Bousi~Prolog Aplications 
There are several practical applications for a language with the aforementioned characteristics: flexible query answering; advanced pattern matching; information retrieval where textual information is selected or analyzed using an ontology; text cataloging and analysis; etc.