PROLOG-CC: um sistema pericial difuso aplicado à ciência do solo

As técnicas da inteligência artificial são ferramentas computacionais apropriadas para modelar processos de transformação que ocorrem na natureza. Tal fato sugere o desenvolvimento de um sistema pericial suportado pelo paradigma da programação em lógica, baseado em regras com raciocínio difuso, possibilitando captar o conhecimento dos peritos. O sistema pericial proposto neste artigo consiste em regras que simulam o conhecimento e o processo de raciocínio do perito humano. São descritos os processos de codificação, inferência e descodificação de um sistema pericial difuso aplicável à análise de fertilidade dos solos. A estimativa do valor final é calculada mediante o mecanismo de agregação de regras denominado Descodificador de Peso Ordenado – DPO. Este sistema pericial é um protótipo que pode ser expandido para sistemas de maior complexidade como, por exemplo, a engenharia de biossistemas, visando automatizar as análises periciais.Artificial intelligence techniques are appropriate computational tools for modelling transformation processes that occur in nature. That fact alone suggests the development of an expert system supported by the logic programming paradigm, based on fuzzy reasoning rules, which enables expert knowledge encoding. The expert system proposed in this paper consists of rules that simulate the knowledge and reasoning processes of an human expert. We describe the encoding, inference and decoding processes of a fuzzy expert system that applies to the analysis of soil fertility. The estimated final value is calculated through the mechanism of rule aggregation called Ordered Weight Decoder - OWD. This expert system is a prototype that can be extended to more complex systems such as, for example, biosystems engineering, aiming to automate the expert analysis

A Transformation-based Implementation for CLP with Qualification and Proximity

Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical LP paradigm. However, few of these are designed as extensions of the well-established and powerful CLP scheme for Constraint Logic Programming. In a previous work we have proposed the SQCLP (proximity-based qualified constraint logic programming) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple-and working-examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications.Comment: 49 pages, 5 figures, 1 table, preliminary version of an article of the same title, published as Technical Report SIC-4-10, Universidad Complutense, Departamento de Sistemas Inform\'aticos y Computaci\'on, Madrid, Spai

Unfolding-based Improvements on Fuzzy Logic Programs

AbstractUnfolding is a semantics-preserving program transformation technique that consists in the expansion of subexpressions of a program using their own definitions. In this paper we define two unfolding-based transformation rules that extend the classical definition of the unfolding rule (for pure logic programs) to a fuzzy logic setting. We use a fuzzy variant of Prolog where each program clause can be interpreted under a different (fuzzy) logic. We adapt the concept of a computation rule, a mapping that selects the subexpression of a goal involved in a computation step, and we prove the independence of the computation rule. We also define a basic transformation system and we demonstrate its strong correctness, that is, original and transformed programs compute the same fuzzy computed answers. Finally, we prove that our transformation rules always produce an improvement in the efficiency of the residual program, by reducing the length of successful Fuzzy SLD-derivations

Un estudio sobre la inclusión de Conjuntos Borrosos en Prolog

Este artículo presenta un estudio de la implementación de Prolog Borrosos, su semántica y sintáctica, con la finalidad de hacer una difusión sobre la combinación de la programación lógica con lógica borrosa que permiten la inclusión de información con razonamiento impreciso e incierto.Palabras clave: Programación lógica, Prolog, lógica borrosa, razonamiento impreciso

Monotone Logic Programming

We propose a notion of an abstract logic. Based on this notion, we define abstract logic programs to be sets of sentences of an abstract logic. When these abstract logics possess certain logical properties (some properties considered are compactness, finitariness, and monotone consequence relations) we show how to develop a fixed-point, model-state-theoretic and proof theoretic semantics for such programs. The work of Melvin Fitting on developing a generalized semantics for multivalued logic programming is extended here to arbitrary abstract logics. We present examples to show how our semantics is robust enough to be applicable to various non-classical logics like temporal logic and multivalued logics, as well as to extensions of classical logic programming such as disjunctive logic programming. We also show how some aspects of the declarative semantics of distributed logic programming, particularly work of Ramanujam, can be incorporated into our framework