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.

Interpretation of Logic Programs Roberto Barbuti , Roberto Giacobazzi , Giorgio Levi Dipartimento di Informatica Universit`a di Pisa Corso Italia 40, 56125 Pisa fbarbuti,giaco,levig@di.unipi.it in ACM Transactions on Programming Languages and Systems Vol 15, January 1993 Abstract The theory of abstract interpretation provides a formal framework to develop advanced dataflow analysis tools. The idea is to define a non-standard semantics which is able to compute, in finite time, an approximated model of the program. In this paper we define an abstract interpretation framework based on a fixpoint approach to the semantics. This leads to the definition, by means of a suitable set of operators, of an abstract fixpoint characterization of a -model associated with the program. Thus, we obtain a specializable abstract framework for bottom-up abstract interpretations of definite logic programs. The specialization of the framework is shown on two examples, namely ground dependence analysis and depth-k analysis.

We present an overview of the program transformation methodology, focusing our attention on the so-called `rules + strategies' approach in the case of functional and logic programs. The paper is intended to offer an introduction to the subject. The various techniques we present are illustrated via simple examples. A preliminary version of this report has been published in: Moller, B., Partsch, H., and Schuman, S. (eds.): Formal Program Development. Lecture Notes in Computer Science 755, Springer Verlag (1993) 263--304. Also published in: ACM Computing Surveys, Vol 28, No. 2, June 1996. 3 1 Introduction The program transformation approach to the development of programs has first been advocated by [Burstall-Darlington 77], although the basic ideas were already presented in previous papers by the same authors [Darlington 72, Burstall-Darlington 75]. In that approach the task of writing a correct and efficient program is realized in two phases: the first phase consists in writing an in...

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.

Abstract: While the ability to simulate nondeterminism and compute multiple solutions for a single query is a powerful and attractive feature of logic programming languages, it is expensive in both time and space. Since programs in such languages are very often functional, i.e. do not produce more than one distinct solution for a single input, this overhead is especially undesirable. This paper describes how pro-grams may be analyzed statically to determine which literals and predicates are functional, and how the program may then be optimized using this information. Our notion of ‘‘functionality’ ’ subsumes the notion of ‘‘determinacy’ ’ that has been considered by various researchers. Our algorithm is less reliant on language features such as the cut, and thus extends more easily to parallel execution strategies, than others that have been proposed.

interpreter for logic programs (Sterling & Shapiro, 1986)...................138 1 1. INTRODUCTION The most important outcome of AI research during the 70s was the general acceptance of the major role of knowledge in intelligent systems (Buchanan & Feigenbaum, 1982). Lenat and Feigenbaum (1989) call this belief the knowledge as power hypothesis and assert it as: "The knowledge principle (KP) A system exhibits intelligent understanding and action at a high level of competence primarily because of the specific knowledge that it can bring to bear: the concepts, facts, representations, methods, models, metaphors, and heuristics about its domain of endeavor." Or as Buchanan and Feigenbaum (Buchanan & Feigenbaum, 1982) put it, "the power of an intelligent program to perform its task well depends primarily on the quantity and quality of knowledge it has about that task." Thus, it is not surprising that the general attitude toward knowledge was a greedy one - grab as much knowledge as you ca...

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.

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.

this paper, we present a transformational approach for proving termination of logic programs by reducing the termination problem of logic programs to that of term rewriting systems. The termination problem of term rewriting systems has been well studied and many useful techniques and tools have been developed for proving termination of term rewriting systems. The prime motivation of our approach is to facilitate the use of this vast source of termination techniques and tools in proving termination of logic programs.

. We consider how mode (such as input and output) and termination properties of typed higher-order constraint logic programming languages may be declared and checked effectively. The systems that we present have been validated through an implementation and numerous case studies. 1 Introduction Just like other paradigms logic programming benefits tremendously from types. Perhaps most importantly, types allow the early detection of errors when a program is checked against a type specification. With some notable exceptions most type systems proposed for logic programming languages to date (see [18]) are concerned with the declarative semantics of programs, for example, in terms of many-sorted, order-sorted, or higher-order logic. Operational properties of logic programs which are vital for their correctness can thus neither be expressed nor checked and errors will remain undetected. In this paper we consider how the declaration and checking of mode (such as input and output) and termina...