Design and implementation of a fuzzy logic programming language using weak unification

Bousi[formula omitted]Prolog: Design and implementation of a proximity-based fuzzy logic programming language

Logic programming (LP) has been successfully applied to solve many problems in artificial intelligence and many other areas. However, LP is unable to deal with uncertain, imprecise or vague information. On the other hand, fuzzy sets and fuzzy logic have demonstrated their applicability in dealing with uncertainty, and have motivated the research on extending classic LP to add fuzzy reasoning. However, a full fledged implementation of a fuzzy logic programming language universally accepted and/or in production does not exist yet. There are many reasons for this, and we present some of them. The aim of the paper is to briefly survey some of the major design problems we faced in implementing a fuzzy version of a Prolog-like logic programming language.

On fuzzy implication relations

Definition of programming languages consists of the formal definition of syntax and semantics. One of the most popular semantic methods used in various stages of software engineering is structural operational semantics. It describes program behavior in the form of state changes after execution of elementary steps of program. This feature makes structural operational semantics useful for implementation of programming languages and also for verification purposes. In our paper we present a new approach to structural operational semantics. We model behavior of programs in category of states, where objects are states, an abstraction of computer memory and morphisms model state changes, execution of a program in elementary steps. The advantage of using categorical model is its exact mathematical structure with many useful proved properties and its graphical illustration of program behavior as a path, i.e. a composition of morphisms. Our approach is able to accentuate dynamics of structural operational semantics. For simplicity, we assume that data are intuitively typed. Visualization and facility of our model is not only a new model of structural operational semantics of imperative programming languages but it can also serve for education purposes.

Software languages have always been an essential component of model-driven engineering. Their importance and popularity has been on the rise thanks to language workbenches, language-oriented development and other methodologies that enable us to quickly and easily create new languages specific for each domain. Unfortunately, language design is largely a form of art and has resisted most attempts to turn it into a form of science or engineering. In this paper we borrow concepts, techniques and principles from the domain of persuasive technology, or wider yet, design with intent --- which was developed as a way to influence users behaviour for social and environmental benefit. Similarly, we claim, software language designers can make conscious choices in order to influence the behaviour of language users. The paper describes a process of extracting design components from 24 books of eight categories (dragon books, parsing techniques, compiler construction, compiler design, language implementation, language documentation, programming languages, software languages), as well as from the original set of Design with Intent cards and papers on DSL design. The resulting language design card toolkit can be used by DSL designers to cover important design decisions and make them with more confidence.

In this paper we present a proposal of a flexible query answering system based on fuzzy logic. We introduce a fuzzy logic programming with a threshold cut and with connectives being arbitrary conjunctors, disjunctors (i.e. they do not need to be associative) and implicators. We use three types of unification: first of syntactical strings based on a similarity relation; second based on linguistic variables; third based on fuzzy conceptual lattices. Our systems assumes a fuzzy logic programming interpreter runs at the remote site. We propose a query dialogue where user can specify thresholds, number of truth values, finite (linear) approximations of fuzzy sets and connectives which are subject of tuning and learning, fuzzy conceptual lattice, fuzzy similarity relations and the form of answer required. This is intended to lower communication (retrieval) complexity. We present a computational (procedural) semantics which is sound and complete wrt. to declarative semantic.

This paper extends fuzzy logic programs [12, 24] to allow the explicit representation of classical negation as well as non-monotonic negation, by introducing the notion of extended fuzzy logic programs. We present the fuzzy answer set semantics for the extended fuzzy logic programs, which is based on the classical answer set semantics of classical extended logic programs [7]. We show that the proposed semantics is a natural extension to the classical answer set semantics of classical extended logic programs [7]. Furthermore, we define fixpoint semantics for extended fuzzy logic programs with and without non-monotonic negation, and study their relationship to the fuzzy answer set semantics. In addition, we show that the fuzzy answer set semantics is reduced to the stable fuzzy model semantics for normal fuzzy logic programs introduced in [42]. The importance of that is computational methods developed for normal fuzzy logic programs can be applied to the extended fuzzy logic programs. Moreover, we show that extended fuzzy logic programs can be intuitively used for representing and reasoning about actions in fuzzy environment.

In large-scale software applications, programmers often combine different programming languages because this allows them to use the most suitable language for a given problem, to gradually migrate existing projects from one language to another, or to reuse existing source code. However, different programming languages have fundamentally different implementations, which are hard to combine. The composition of language implementations often results in complex interfaces between languages, insufficient flexibility, or poor performance. We propose TruffleVM, a virtual machine (VM) that can execute different programming languages and is able to compose them in a seamless way. TruffleVM supports dynamically-typed languages (e.g., JavaScript and Ruby) as well as statically typed low-level languages (e.g., C). It consists of individual language implementations, which translate source code to an intermediate representation that is executed by a shared VM. TruffleVM composes these different language implementations via generic access . Generic access is a language-agnostic mechanism that language implementations use to access foreign data or call foreign functions. It features language-agnostic messages that the TruffleVM resolves to efficient foreign-language-specific operations at runtime. Generic access supports multiple languages, enables an efficient multi-language development, and ensures high performance. We evaluate generic access with two case studies. The first one explains the transparent composition of JavaScript, Ruby, and C. The second one shows an implementation of the C extensions application programming interface (API) for Ruby. We show that generic access guarantees good runtime performance. It avoids conversion or marshalling of foreign objects at the language boundary and allows the dynamic compiler to perform its optimizations across language boundaries.

This volume contains the proceedings of the 21th Brazilian Symposium on Programing Languages (SBLP 2017), held during September 21-22, 2017, at Fortaleza, Brazil. SBLP is a well-established symposium, promoted by the Brazilian Computer Society since 1996, and provides a venue for researchers and practitioners interested in the fundamental principles and innovations in the design and implementation of programming languages and systems. Since 2010, SBLP has been organized in the context of CBSoft (Brazilian Conference on Software: Theory and Practice), colocated with a number of other events on computer science and software engineering.

The T project is an experiment in language design and implementation. Its purpose is to test the thesis developed by Steele and Sussman in their series of papers about the Scheme language: that Scheme may be used as the basis for a practical programming language of exceptional expressive power; and, that implementations of Scheme could perform better than other Lisp systems, and competitively with implementations of programming languages, such as C and Bliss, which are usually considered to be inherently more efficient than Lisp on conventional machine architectures. We are developing a portable implementation of T, currently targeted for the VAX under the Unix and VMS operating systems and for the Apollo, a MC68000-based workstation.

In this chapter we outline a process algebra, whose name — Petri Box Calculus (PBC) — arises from its original Petri net semantics [6, 7]. PBC combines a number of features taken from other process algebras, such as COSY (Concurrent Systems notation, [62]), CCS (Calculus of Communicating Systems, [80, 81]), SCCS (Synchronous CCS, [81]), CSP (Communicating Sequential Processes, [58]), TCSP (Theoretical CSP, [59]) and ACP (Algebra of Communicating Processes, [2]). However, there are some fundamental differences with each of these process algebras since PBC has been designed with two specific objectives in mind: To support a compositional Petri net semantics, together with an equivalent — more syntax-oriented — structured operational semantics (SOS) [89]. To provide as flexible as possible a basis for developing a compositional semantics of high-level concurrent specification and programming languages with full data and control features.

We start this overview with a discussion of the computational rule of inference [Zadeh 1973] which plays a crucial role in fuzzy control (see below) but also in approximate reasoning [Fuller & Zimmermann 1992]. It is a way to associate with a fuzzy input a fuzzy output by means of a fuzzy relation between the input and the output space. We also give a geometric and a logical interpretation of the computational rule of inference, the latter showing a close relationship to the R-implications studied in Section 11.1. If input and output are known, we present criteria for the solvability of the corresponding fuzzy relational equation (see [Sanchez 1974, 1976, 1977, 1984] and [Gottwald 1986, Di Nola et al. 1989]).

Modelling with fuzzy relations in approximate reasoning is obstructed sometimes by the inconsistency of obtained fuzzy relational equations. This paper tackles the inconsistency resolving problem for a finite system of max–min equations by modifying only the right-hand side vector as slightly as possible with respect to the sum of absolute deviations. It is demonstrated that this problem may be reformulated equivalently as a polynomial-sized mixed integer linear programming problem. Although such a reformulation results in a problem of much larger size than its original compact form, it may be solved to optimality on instances of moderate size or even large size by an off-the-shelf solver for mixed integer linear programming and in some sense does not require a tailored solving method.

Aiming to deal with the issue that troubles the knowledge accumulation in a lot of complicated systems in the form of expertise that is showed as the fuzzy language, and fuzzy neural network (FNN) based on the traditional fuzzy algorithm is difficult to utilize expertise to get abundant training samples directly, this thesis proposes the FNN model based on the improved fuzzy algorithm. In this model, the improved fuzzy algorithm is not only used to deal with the input amount, but also to directly change the expertise into training sample that is necessary in neural network training. Then, a FNN diagnosis model based on the improved fuzzy algorithm is put forward. Compared it with the model based on the traditional fuzzy algorithm, the model designed in this thesis proves to be more effective, more clearly reasoned and more quickly in analysis. Moreover, it can produce sufficient training samples. All of these advantages will be useful to the accumulation of experience, online training and the improvement of FNN diagnosis precision.

The Programming Languages (PL) Track provides researchers and practitioners with a forum to present their ideas and experience in designing new programming concepts and implementing programming languages. It includes the topics of Compiling Techniques, Domain-Specific Languages, Formal Semantics and Syntax, Garbage Collection, Language Design and Implementation, Languages for Modeling, Model-Driven Development and Model Transformation, New Programming Language Ideas and Concepts, New Programming Paradigms, Practical Experiences with Programming Languages, Program Analysis and Verification, Program Generation and Transformation, Programming Languages from All Paradigms (Agent-Oriented, Aspect-Oriented, Functional, Logic, Object-Oriented, etc.), and Visual Programming Languages.