scholarly journals Optimality in goal-dependent analysis of Sharing

2009 ◽  
Vol 9 (05) ◽  
pp. 617-689 ◽  
Author(s):  
GIANLUCA AMATO ◽  
FRANCESCA SCOZZARI
Keyword(s):  
Logic Programming ◽  
Static Analysis ◽  
Abstract Interpretation ◽  
Logic Programs ◽  
Semantic Operators

AbstractWe face the problems of correctness, optimality, and precision for the static analysis of logic programs, using the theory of abstract interpretation. We propose a framework with a denotational, goal-dependent semantics equipped with two unification operators for forward unification (calling a procedure) and backward unification (returning from a procedure). The latter is implemented through a matching operation. Our proposal clarifies and unifies many different frameworks and ideas on static analysis of logic programming in a single, formal setting. On the abstract side, we focus on the domainsharingby Jacobs and Langen (The Journal of Logic Programming, 1992, vol. 13, nos. 2–3, pp. 291–314) and provide the best correct approximation of all the primitive semantic operators, namely, projection, renaming, and forward and backward unifications. We show that the abstract unification operators are strictly more precise than those in the literature defined over the same abstract domain. In some cases, our operators are more precise than those developed for more complex domains involving linearity and freeness.

RECOGNIZING NON-FLOUNDERING LOGIC PROGRAMS AND GOALS

1990 ◽  
Vol 01 (02) ◽  
pp. 151-163 ◽  
Author(s):  
ROBERTO BARBUTI ◽  
MAURIZIO MARTELLI
Keyword(s):  
Logic Programming ◽  
Abstract Interpretation ◽  
Logic Programs ◽  
Negation As Failure ◽  
Proof Procedure ◽  
Resolution Proof

The introduction of negation in Logic Programming using the Negation as Failure Rule causes some problems regarding the completeness of the SLDNF-Resolution proof procedure. One of the causes of incompleteness arises when evaluating a non-ground negative literal. This is solved by forbidding these evaluations. Obviously, there is the possibility of having only non-ground negative literals in the goal (the floundering of the goal). There is a class of programs and goals (allowed) that has been proved to have the non-floundering property. In this paper an algorithm is proposed which recognizes a wider class of programs with this property and which is based on abstract interpretation techniques.

scholarly journals Evaluation of the Implementation of an Abstract Interpretation Algorithm using Tabled CLP

2019 ◽  
Vol 19 (5-6) ◽  
pp. 1107-1123
Author(s):  
JOAQUÍN ARIAS ◽  
MANUEL CARRO
Keyword(s):  
Logic Programming ◽  
Abstract Interpretation ◽  
Constraint Logic Programming ◽  
Additional Cost ◽  
Logic Programs ◽  
Semantic Equivalence ◽  
Constraint Solver ◽  
Abstract Domains ◽  
Interpretation Algorithm ◽  
Branch Switching

AbstractCiaoPP is an analyzer and optimizer for logic programs, part of the Ciao Prolog system. It includes PLAI, a fixpoint algorithm for the abstract interpretation of logic programs which we adapt to use tabled constraint logic programming. In this adaptation, the tabling engine drives the fixpoint computation, while the constraint solver handles the LUB of the abstract substitutions of different clauses. That simplifies the code and improves performance, since termination, dependencies, and some crucial operations (e.g., branch switching and resumption) are directly handled by the tabling engine. Determining whether the fixpoint has been reached uses semantic equivalence, which can decide that two syntactically different abstract substitutions represent the same element in the abstract domain. Therefore, the tabling analyzer can reuse answers in more cases than an analyzer using syntactical equality. This helps achieve better performance, even taking into account the additional cost associated to these checks. Our implementation is based on the TCLP framework available in Ciao Prolog and is one-third the size of the initial fixpoint implementation in CiaoPP. Its performance has been evaluated by analyzing several programs using different abstract domains.

scholarly journals An effective fixpoint semantics for linear logic programs

2001 ◽  
Vol 2 (1) ◽  
pp. 85-122 ◽  
Author(s):  
MARCO BOZZANO ◽  
GIORGIO DELZANNO ◽  
MAURIZIO MARTELLI
Keyword(s):  
Logic Programming ◽  
Petri Nets ◽  
Abstract Interpretation ◽  
Theoretical Foundation ◽  
Linear Logic ◽  
Sufficient Conditions ◽  
Logic Programs ◽  
Bottom Up ◽  
Interesting Class ◽  
Fixpoint Semantics

In this paper we investigate the theoretical foundation of a new bottom-up semantics for linear logic programs, and more precisely for the fragment of LinLog (Andreoli, 1992) that consists of the language LO (Andreoli & Pareschi, 1991) enriched with the constant 1. We use constraints to symbolically and finitely represent possibly infinite collections of provable goals. We define a fixpoint semantics based on a new operator in the style of TP working over constraints. An application of the fixpoint operator can be computed algorithmically. As sufficient conditions for termination, we show that the fixpoint computation is guaranteed to converge for propositional LO. To our knowledge, this is the first attempt to define an effective fixpoint semantics for linear logic programs. As an application of our framework, we also present a formal investigation of the relations between LO and Disjunctive Logic Programming (Minker et al., 1991). Using an approach based on abstract interpretation, we show that DLP fixpoint semantics can be viewed as an abstraction of our semantics for LO. We prove that the resulting abstraction is correct and complete (Cousot & Cousot, 1977; Giacobazzi & Ranzato, 1997) for an interesting class of LO programs encoding Petri Nets.

A Survey of Parametric Static Analysis

2021 ◽  
Vol 54 (7) ◽  
pp. 1-37
Author(s):  
Jihyeok Park ◽  
Hongki Lee ◽  
Sukyoung Ryu
Keyword(s):  
Recent Work ◽  
Static Analysis ◽  
Abstract Interpretation ◽  
Black Box ◽  
Parameter Selection ◽  
Survey Analysis ◽  
Future Directions ◽  
Analysis Techniques ◽  
Analysis Quality

Understanding program behaviors is important to verify program properties or to optimize programs. Static analysis is a widely used technique to approximate program behaviors via abstract interpretation. To evaluate the quality of static analysis, researchers have used three metrics: performance, precision, and soundness. The static analysis quality depends on the analysis techniques used, but the best combination of such techniques may be different for different programs. To find the best combination of analysis techniques for specific programs, recent work has proposed parametric static analysis . It considers static analysis as black-box parameterized by analysis parameters , which are techniques that may be configured without analysis details. We formally define the parametric static analysis, and we survey analysis parameters and their parameter selection in the literature. We also discuss open challenges and future directions of the parametric static analysis.

A Modal Semantics of Negation in Logic Programming

1992 ◽  
Vol 16 (3-4) ◽  
pp. 231-262
Author(s):  
Philippe Balbiani
Keyword(s):  
Modal Logic ◽  
Logic Programming ◽  
Modal Logics ◽  
Kripke Models ◽  
Logic Programs ◽  
Modal Semantics ◽  
Soundness And Completeness ◽  
Modal Completion ◽  
Well Foundedness

The beauty of modal logics and their interest lie in their ability to represent such different intensional concepts as knowledge, time, obligation, provability in arithmetic, … according to the properties satisfied by the accessibility relations of their Kripke models (transitivity, reflexivity, symmetry, well-foundedness, …). The purpose of this paper is to study the ability of modal logics to represent the concepts of provability and unprovability in logic programming. The use of modal logic to study the semantics of logic programming with negation is defended with the help of a modal completion formula. This formula is a modal translation of Clack’s formula. It gives soundness and completeness proofs for the negation as failure rule. It offers a formal characterization of unprovability in logic programs. It characterizes as well its stratified semantics.

scholarly journals Learning Large Logic Programs By Going Beyond Entailment

2020 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančic
Keyword(s):  
Logic Programming ◽  
Loss Function ◽  
Inductive Logic Programming ◽  
State Of The Art ◽  
Inductive Logic ◽  
Program Synthesis ◽  
Loss Functions ◽  
Logic Programs ◽  
Binary Decision ◽  
Best First Search

A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use 'example-dependent' loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems.

scholarly journals Bottom-up abstract interpretation of logic programs

1994 ◽  
Vol 124 (1) ◽  
pp. 93-125 ◽  
Author(s):  
Michael Codish ◽  
Dennis Dams ◽  
Eyal Yardeni
Keyword(s):  
Abstract Interpretation ◽  
Logic Programs ◽  
Bottom Up
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