Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.

A practical procedure for computing a regular approximation of a logic program is given. Regular approximations are useful in a variety of tasks in debugging, program specialisation and compile-time optimisation. The algorithm shown here incorporates optimisations taken from deductive database fixpoint algorithms and efficient bottom-up abstract interpretation techniques. Frameworks for defining regular approximations have been put forward in the past, but the emphasis has usually been on theoretical aspects. Our results contribute mainly to the development of effective analysis tools that can be applied to large programs. Precision of the approximation can be greatly improved by applying query-answer transformations to a program and a goal, thus capturing some argument dependency information. A novel technique is to use transformations based on computation rules other than left-to-right to improve precision further. We give performance results for our procedure on a range of programs. 1

Abstract: Mode and data dependency analyses find many applications in the generation of efficient exe-cutable code for logic programs. For example, mode information can be used to generate specialized unification instructions where permissible; to detect determinacy and functionality of programs; to gen-erate index structures more intelligently; to reduce the amount of runtime tests in systems that support goal suspension; and in the integration of logic and functional languages. Data dependency information can be used for various source-level optimizing transformations, to improve backtracking behavior, and to parallelize logic programs. This paper describes and proves correct an algorithm for the static infer-ence of modes and data dependencies in a program. The algorithm is shown to be quite efficient for pro-grams commonly encountered in practice.

In general, logic programs are undirected, i.e. there is no concept of "input" and "output" arguments to a procedure. An argument may be used either as an input or as an output argument, and programs may be executed either in a "forward" direction or in a "backward" direction. However, it is often the case that in a given program, a predicate is used with some of its arguments used consistently as input arguments and others as output arguments. Such mode information can be used by a compiler to effect various optimizations. This paper considers the problem of automatically inferring the modes of the predicates in a program. The dataflow analysis we use is more powerful than approaches relying on syntactic characteristics of programs, e.g. [18]. Our work differs from that of Mellish [14, 15] in that (i) we give a sound and efficient treatment of variable aliasing in mode inference; (ii) by propagating instantiation information using state transformations rather than through dependen...

Abstract This paper presents a formal framework for the bottom-up abstract interpretation of logic programs which can be applied to approximate answer substitutions, partial an- swer substitutions and call patterns for a given program and arbitrary initial goal. The framework is based on a T P like semantics defined over a Herbrand universe with vari-ables which has previously been shown to determine the answer substitutions for arbitrary initial goals. The first part of the paper reconstructs this semantics to provide a more adequate basis for abstract interpretation. A notion of abstract substitution is introduced and shown to determine an abstract semantic function which for a given program can be applied to approximate the answer substitutions for an arbitrary initial goal.

Since the early days of logic programming, researchers in the field realized the potential for exploitation of parallelism present in the execution of logic programs. Their high-level nature, the presence of non-determinism, and their referential transparency, among other characteristics, make logic programs interesting candidates for obtaining speedups through parallel execution. At the same time, the fact that the typical applications of logic programming frequently involve irregular computations, make heavy use of dynamic data structures with logical variables, and involve search and speculation, makes the techniques used in the corresponding parallelizing compilers and run-time systems potentially interesting even outside the field. The objective of this paper is to provide a comprehensive survey of the issues arising in parallel execution of logic programming languages along with the most relevant approaches explored to date in the field. Focus is mostly given to the challenges emerging from the parallel execution of Prolog programs. The paper describes the major techniques used for shared memory implementation of Or-parallelism, And-parallelism, and combinations of the two. We also explore some related issues, such as memory

Abstract: The semantics of Prolog programs is usually given in terms of the model theory of first order logic. However, this does not adequately characterize the computational behavior of Prolog programs. Prolog implementations typically use a sequential evaluation strategy based on the textual order of clauses and literals in a program, as well as non-logical features like ‘‘cut’’. In this work we develop a denotational semantics that captures the computational behavior of Prolog. We present a semantics for ‘‘cut-free’ ’ Prolog, which is then extended to Prolog with cut. For each case we develop a congruence proof that relates the semantics to a standard operational interpreter. As an application of our denotational semantics, we show the correctness of some standard ‘‘folk’ ’ theorems regarding transformations on Pro-log programs.

This article reports some results on this correlation in the context of logic programs. A formal notion of the "precision" of an analysis algorithm is proposed, and this is used to characterize the worst-case computational complexity of a number of dataflow analyses with different degrees of precision. While this article considers the analysis of logic programs, the technique proposed, namely the use of "exactness sets" to study relationships between complexity and precision of analyses, is not specific to logic programming in any way, and is equally applicable to flow analyses of other language families.

Functional and logic programming are the most important declarative programming paradigms, and interest in combining them has grown over the last decade. Early research concentrated on the definition and improvement of execution principles for such integrated languages, while more recently efficient implementations of these execution principles have been developed so that these languages became relevant for practical applications. In this paper we survey the development of the operational semantics as well as

Program transformation is a methodology for deriving correct and efficient programs from specifications. In this chapter, we will look at the so called 'rules + strategies' approach, and we will report on the main techniques which have been introduced in the literature for that approach, in the case of logic programs. We will also present some examples of program transformation, and we hope that through those examples the reader may acquire some familiarity with the techniques we will describe.