Static and Dynamic Slicing of Constraint Logic Programs

Slicing is a program analysis technique originally developed for imperative languages. It facilitates understanding of data flow and debugging.This paper discusses slicing of Constraint Logic Programs. Constraint Logic Programming (CLP) is an emerging software technology with a growing number of applications. Data flow in constraint programs is not explicit, and for this reason the concepts of slice and the slicing techniques of imperative languages are not directly applicable.This paper formulates declarative notions of slice suitable for CLP. They provide a basis for defining slicing techniques (both dynamic and static) based on variable sharing. The techniques are further extended by using groundness information.A prototype dynamic slicer of CLP programs implementing the presented ideas is briefly described together with the results of some slicing experiments.     

A practical object-oriented analysis engine for CLP

The incorporation of global program analysis into recent compilers for Constraint Logic Programming (CLP) languages has greatly improved the efficiency of compiled programs. We present a global analyser based on abstract interpretation. Unlike traditional optimizers, whose designs tend to be ad hoc, the analyser has been designed with flexibility in mind. The analyser is incremental, allowing substantial program transformations by a compiler without requiring redundant re-computation of analysis data. The analyser is also generic in that it can perform a large number of different program analyses. Furthermore, the analyser has an object-oriented design, enabling it to be adapted to different applications easily and allowing it to be used with various CLP languages with simple modifications. As an example of this generality, we sketch the use of the analyser in two different applications involving two distinct CLP languages: an optimizing compiler for CLP(R) programs and an application for detecting occur-check problems in Prolog programs. © 1998 John Wiley & Sons Ltd.     

Probabilistic logic programming on the web

We present the web application ‘cplint on SWI‐Prolog for SHaring that allows the user to write (SWISH)' Probabilistic Logic Programs and submit the computation of the probability of queries with a web browser. The application is based on SWISH, a web framework for Logic Programming. SWISH is based on various features and packages of SWI‐Prolog, in particular, its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system, which is included in cplint , a suite of programs for reasoning over Logic Programs with Annotated Disjunctions, by porting PITA to SWI‐Prolog. Moreover, we modified the PITA library so that it can be executed in a multi‐threading environment. Developing ‘cplint on SWISH’ also required modification of the JavaScript SWISH code that creates and queries Pengines. ‘cplint on SWISH’ includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic Logic Programming. By providing a web interface to cplint , we allow users to experiment with Probabilistic Logic Programming without the need to install a system, a procedure that is often complex, error prone, and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize Probabilistic Logic Programming. Copyright © 2015 John Wiley & Sons, Ltd.     

Extending expressivity and flexibility of abductive logic programming

Real-world problems often require purely deductive reasoning to be supported by other techniques that can cope with noise in the form of incomplete and uncertain data. Abductive inference tackles incompleteness by guessing unknown information, provided that it is compliant with given constraints. Probabilistic reasoning tackles uncertainty by weakening the sharp logical approach. This work aims at bringing both together and at further extending the expressive power of the resulting framework, called Probabilistic Expressive Abductive Logic Programming (PEALP). It adopts a Logic Programming perspective, introducing several kinds of constraints and allowing to set a degree of strength on their validity. Procedures to handle both extensions, compatibly with standard abductive and probabilistic frameworks, are also provided.     

Aggregation and negation-as-failure

Set-grouping and aggregation are powerful operations of practical interest in database query languages. An aggregate operation is a function that maps a set to some value, e.g., the maximum or minimum in the set, the cardinality of this set, the summation of all its members, etc. Since aggregate operations are typically non-monotonic in nature, recursive programs making use of aggregate operations must be suitably restricted in order that they have a well-defined meaning. In a recent paper we showed that partial-order clauses provide a well-structured means of formulating aggregate operations with recursion. In this paper, we consider the problem of expressing partial-order programs via negation-as-failure (NF), a well-known non-monotonic operation in logic programming. We show a natural translation of partial-order programs to normal logic programs: Anycost-monotonic partial-order programsP is translated to astratified normal program such that the declarative semantics ofP is defined as the stratified semantics of the translated program. The ability to effect such a translation is significant because the resulting normal programs do not make any explicit use of theaggregation capability, yet they are concise and intuitive. The success of this translation is due to the fact that the translated program is a stratified normal program. That would not be the case for other more general classes of programs thancost-monotonic partial-order programs. We therefore develop in stages a refined translation scheme that does not require the translated programs to be stratified, but requires the use of a suitable semantics. The class of normal programs originating from this refined translation scheme is itself interesting: Every program in this class has a clear intended total model, although these programs are in general neither stratified nor call-consistent, and do not have a stable model. The partial model given by the well-founded semantics is consistent with the intended total model and the extended well founded semantics,WFS ⁺, defines the intended model. Since there is a well-defined and efficient operational semantics for partial-order programs^{14, 15, 21)} we conclude that the gap between expression of a problem and computing its solution can be reduced with the right level of notation. Mauricio J. Osorio G., Ph.D.: He is an Associate Professor in the Department of Computer Systems Engineering, University of the Americas, Puebla, Mexico. He is the Head of the Laboratory of Theoretical Computer Science of the Center of Research (CENTIA), Puebla, Mexico. His research is currently funded by CENTIA and CONACYT (Ref. #C065-E9605). He is interested in Applications of Logic to Computer Science, with special emphasis on Logic Programming. He received his B.Sc. in Computer Science from the Universidad Autonoma de Puebla, his M.Sc. in Electrical Engineering from CINVESTAV in Mexico, and his Ph.D. from the State University of New York at Buffalo in 1995. Bharat Jayaraman, Ph.D.: He is an Associate Professor in the Department of Computer Science at the State University of New York at Buffalo. He obtained his bachelors degree in Electronics from the Indian Institute of Technology, Madras in 1975, and his Ph.D. from the University of Utah in 1981. His research interests are in Programming Languages and Declarative Modeling of Complex Systems. He has published over 50 research papers. He has served on the program committees of several conferences in the area of Programming Languages, and he is presently on the Editorial Board of the Journal of Functional and Logic Programming.     

Constructive negation and constraint logic programming with sets

The aim of this paper is to extend theConstructive Negation technique to the case ofCLP(SεT), a Constraint Logic Programming (CLP) language based on hereditarily (and hybrid) finite sets. The challenging aspects of the problem originate from the fact that the structure on whichCLP(SεT) is based is notadmissible closed, and this does not allow to reuse the results presented in the literature concerning the relationships betweenCLP and constructive negation. We propose a new constraint satisfaction algorithm, capable of correctly handling constructive negation for large classes ofCLP(SεT) programs; we also provide a syntactic characterization of such classes of programs. The resulting algorithm provides a novel constraint simplification procedure to handle constructive negation, suitable to theories where unification admits multiple most general unifiers. We also show, using a general result, that it is impossible to construct an interpreter forCLP(SεT) with constructive negation which is guaranteed to work for any arbitrary program; we identify classes of programs for which the implementation of the constructive negation technique is feasible. Agostino Dovier, Ph.D.: He is a researcher in the Department of Science and Technology at the University of Verona, Italy. He obtained his master degree in Computer Science from the University of Udine, Italy, in 1991 and his Ph.D. in Computer Science from the University of Pisa, Italy, in 1996. His research interests are in Programming Languages and Constraints over complex domains, such as Sets and Multisets. He has published over 20 research papers in International Journals and Conferences. He is teaching a course entitled “Special Languages and Techniques for Programming” at the University of Verona. Enrico Pontelli, Ph.D.: He is an Assistant Professor in the Department of Computer Science at the New Mexico State University. He obtained his Laurea degree from the University of Udine (Italy) in 1991, his Master degree from the University of Houston in 1992, and his Ph.D. degree from New Mexico State University in 1997. His research interests are in Programming Languages, Parallel Processing, and Constraint Programming. He has published over 50 papers and served on the program committees of different conferences. He is presently the Associate Director of the Laboratory for Logic, Databases, and Advanced Programming. Gianfranco Rossi, Ph.D.: He received his degree in Computer Science from the University of Pisa in 1979. From 1981 to 1983 he was employed at Intecs Co. System House in Pisa. From November 1983 to February 1989 he was a researcher at the Dipartimento di Informatica of the University of Turin. Since March 1989 he is an Associate Professor of Computer Science, currently with the University of Parma. He is the author of several papers dealing mainly with programming languages, in particular logic programming languages and Prolog, and extended unification algorithms. His current research interests are (logic) programming languages with sets and set unification algorithms.     

Vague Domains, S-Unification and Logic Programming

The purpose of this paper is to give some insights to the use of vague (fuzzy) constants and similarity in logic programming. The basis of semantics for Similarity based Logic Programming (SbLP) is presented. S-unification, or similarity based unification is discussed. The paper also takes a look at the principles of S-resolution with the help of an example.     

A semantical framework for hybrid knowledge bases

In the ongoing discussion about combining rules and ontologies on the Semantic Web a recurring issue is how to combine first-order classical logic with nonmonotonic rule languages. Whereas several modular approaches to define a combined semantics for such hybrid knowledge bases focus mainly on decidability issues, we tackle the matter from a more general point of view. In this paper, we show how Quantified Equilibrium Logic (QEL) can function as a unified framework which embraces classical logic as well as disjunctive logic programs under the (open) answer set semantics. In the proposed variant of QEL, we relax the unique names assumption, which was present in earlier versions of QEL. Moreover, we show that this framework elegantly captures the existing modular approaches for hybrid knowledge bases in a unified way.     

Derivation of Efficient Logic Programs by Specialization and Reduction of Nondeterminism

Program specialization is a program transformation methodology which improves program efficiency by exploiting the information about the input data which are available at compile time. We show that current techniques for program specialization based on partial evaluation do not perform well on nondeterministic logic programs. We then consider a set of transformation rules which extend the ones used for partial evaluation, and we propose a strategy for guiding the application of these extended rules so to derive very efficient specialized programs. The efficiency improvements which sometimes are exponential, are due to the reduction of nondeterminism and to the fact that the computations which are performed by the initial programs in different branches of the computation trees, are performed by the specialized programs within single branches. In order to reduce nondeterminism we also make use of mode information for guiding the unfolding process. To exemplify our technique, we show that we can automatically derive very efficient matching programs and parsers for regular languages. The derivations we have performed could not have been done by previously known partial evaluation techniques.A preliminary version of this paper appears as: Reducing Nondeterminism while Specializing Logic Programs. Proceedings of the 24th Annual ACM Symposium on Principles of Programming Languages, Paris, France, January 15–17, 1997, ACM Press, 1997, pp. 414–427.     

Verifying local stratifiability of logic programs and databases

In this paper, we deal with the problem of verifying local stratifiability of logic programs and databases presented by Przymusinski. The notion of dependency graphs is generalized from representing the priority relation between predicate symbols to representing the priority between atoms. Necessary and sufficient conditions for the local stratifiability of logic programs are presented and algorithms for performing the verification are developed. Finally, we prove that a database DB containing clauses with disjunctive consequents can easily be converted into a logic program P such that DB is locally stratified iff P is locally stratified. Yi-Dong Shen, Dr.: Department of Computer Science, Chongqing University, Chongqing, 630044, P.R. China (Present Address) c/o Ping Ran, Department of Heat Power Engineering, Chongqing UniversityResearch interests: Artificial Intelligence, Deductive Databases, Logic Programming, Non-Monotonic Reasoning, Parallel Processing     

A Language for Multi-dimensional Updates

Dynamic Logic Programming (DLP) was introduced to deal with knowledge about changing worlds, by assigning semantics to sequences of generalized logic programs, each of which represents a state of the world. These states permit the representation, not only of time, but also of specificity, strength of updating instance, hierarchical position of the knowledge source, etc. Subsequently, the Language of Updates LUPS was introduced to allow for the association, with each state, of a set of transition rules. It thereby provides for an interleaving sequence of states and transition rules within an integrated declarative framework. DLP (and LUPS), because defined only for a linear sequence of states, cannot deal simultaneously with more than a single dimension (e.g. time, hierarchies,…). To overcome this limitation, Multi-dimensional Dynamic Logic Programming (MDLP) was therefore introduced, so as to make it possible to organize states into arbitrary acyclic digraphs (DAGs). In this paper we now extend LUPS, setting forth a Language for Multi-dimensional Updates (MLUPS). MLUPS admits the specification of flexible evolutions of such DAG organized logic programs, by allowing not just the specification of the logic programs representing each state, but to the evolution of the DAG topology itself as well.     

Strong equivalence of logic programs under the infinite-valued semantics

We consider the notion of strong equivalence [V. Lifschitz, D. Pearce, A. Valverde, Strongly equivalent logic programs, ACM Transactions on Computational Logic 2 (4) (2001) 526-541] of normal propositional logic programs under the infinite-valued semantics [P. Rondogiannis, W.W. Wadge, Minimum model semantics for logic programs with negation-as-failure, ACM Transactions on Computational Logic 6 (2) (2005) 441-467] (which is a purely model-theoretic semantics that is compatible with the well-founded one). We demonstrate that two such programs are strongly equivalent under the infinite-valued semantics if and only if they are logically equivalent in the corresponding infinite-valued logic. In particular, we show that strong equivalence of normal propositional logic programs is decidable, and more specifically coNP-complete. Our results have a direct implication for the well-founded semantics since, as we demonstrate, if two programs are strongly equivalent under the infinite-valued semantics, then they are also strongly equivalent under the well-founded semantics.     

约束逻辑程序设计综述

一、引言 约束逻辑程序设计(Constraint Logic Program-ming．CLP)是基于人工智能(AI)中约束满足问题(Constraint Satisfaction Problem．CSP)模型的一种程序设计风范。CLP是逻辑程序设计(LP)的一种推广,是八十年代发展起来的一种新的逻辑程序设计方法。由于它继承了LP简单易懂的说明性描述方法并结合了CSP在求解问题时的效率,使它在解决很多AI问题(如组合问题、资源分配、事务安排等)时有不凡的表现。更由于AI领域中绝大多数问题可以用CLP来表示,所以这一方法已引起了人们的广泛注意,并在八十年代后期得以迅速发展。     

Mechanical reasoning about families of UTP theories

The Unifying Theories of Programming (UTP) of Hoare and He is a general framework in which the semantics of a variety of specification and programming languages can be uniformly defined. In this paper we present a semantic embedding of the UTP into the ProofPower-Z theorem prover; it concisely captures the notion of UTP theory, theory instantiation, and, additionally, type restrictions on the alphabet of UTP predicates. We show how the encoding can be used to reason about UTP theories and their predicates, including models of particular specifications and programs. We support encoding and reasoning about combinations of predicates of various theory instantiations, as typically found in UTP models. Our results go beyond what has already been discussed in the literature in that we support encoding of both theories and programs (or their specifications), and high-level proof tactics. We also create structuring mechanisms that support the incremental construction and reuse of encoded theories, associated laws and proof tactics.     

CLP(ℜ) and some electrical engineering problems

The Constraint Logic Programming Scheme defines a class of languages designed for programming with constraints using a logic programming approach. These languages are soundly based on a unified framework of formal semantics. In particular, as an instance of this scheme with real arithmetic constraints, the CLP() language facilitates and encourages a concise and declarative style of programming for problems involving a mix of numeric and non-numeric computation.In this paper we illustrate the practical applicability of CLP() with examples of programs to solve electrical engineering problems. This field is particularly rich in problems that are complex and largely numeric, enabling us to demonstrate a number of the unique features of CLP(). A detailed look at some of the more important programming techniques highlights the ability of CLP() to support well-known, powerful techniques from constraint programming. Our thesis is that CLP() is an embodiment of these techniques in a language that is more general, elegant and versatile than the earlier languages, and yet is practical.An earlier version of this paper appeared in the proceedings of the 4th International Conference on Logic Programming, Melbourne, May 1987. Much of this work was carried out while the authors were at Monash University, Melbourne, Australia.     

A new generic scheme for functional logic programming with constraints

In this paper we propose a new generic scheme CFLP풟, intended as a logical and semantic framework for lazy Constraint Functional Logic Programming over a parametrically given constraint domain 풟. As in the case of the well known CLP풟 scheme for Constraint Logic Programming, 풟 is assumed to provide domain specific data values and constraints. CFLP풟 programs are presented as sets of constrained rewrite rules that define the behavior of possibly higher order and/or non-deterministic lazy functions over 풟. As a main novelty w.r.t. previous related work, we present a Constraint Rewriting Logic CRWL풟 which provides a declarative semantics for CFLP풟 programs. This logic relies on a new formalization of constraint domains and program interpretations, which allows a flexible combination of domain specific data values and user defined data constructors, as well as a functional view of constraints. This research has been partially supported by the Spanish National Projects MELODIAS (TIC2002-01167), MERIT-FORMS (TIN2005-09207-C03-03) and PROMESAS-CAM (S-0505/TIC/0407).     

Intractable unifiability problems and backtracking

Restrictions of the problem of finding all maximal unifiable or minimal nonunifiable subsets to those of certain sizes are shown to be NP-hard, and consequently inappropriate in general for reducing thrashing by intelligent backtracking in resolution theorem provers and logic program executions. We also show that existing backtrack methods based on the computation of all maximal unifiable subsets of assignments as a means to avoid thrashing are intractable because the solution length of these subsets can increase exponentially with the input length, and we give a corresponding result for minimal nonunifiability. The results apply not only to standard unification, but to a characterized collection of unification algorithms which includes unification without the occurs check. This now justifies the necessity for approximate or heuristic approaches to reduce thrashing in resolution theorem provers and the execution of logic programs.A version of this paper appears in Proceedings of the Third International Logic Programming Conference, London, Lecture Notes in Computer Science, Springer 225, 107–121 (1986).