We study the properties of input-consuming derivations of moded logic programs. Input-consuming derivations can be used to model the behavior of logic programs using dynamic scheduling and employing constructs such as delay declarations. We consider the class of nicely-moded programs and queries. We show that for these programs a weak version of the well-known switching lemma holds also for input-consuming derivations. Furthermore, we show that, under suitable conditions, there exists an algebraic characterization of termination of input-consuming derivations.

At present, the search for specific information on the World Wide Web is faced with several problems, which arise on the one hand from the vast number of information sources available, and on the other hand, from their intrinsic heterogeneity, since standards are missing. A promising approach for solving the complex problems emerging in this context is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires advanced capabilities to address complex tasks, such as search and assessment of information sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest lies in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested to see how they can be used, extended, and further developed for the specific needs of this application domain. We review some existing systems and current projects, which typically address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches and methods from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out the possible extensions and open issues.

We propose a modular method for proving termination of general logic programs (i.e. logic programs with negation). It is based on the notion of acceptable programs, but it allows us to prove termination in a truly modular way. We consider programs consisting of a hierarchy of modules and supply a general result for proving termination by dealing with each module separately. For programs which are in a certain sense well-behaved, namely well-moded or well-typed programs, we derive both a simple verification technique and an iterative proof method. Some examples show how our system allows for greatly simplified proofs.

This special issue marks the tenth anniversary of the LOPSTR workshop. LOPSTR started in 1991 as a workshop on Logic Program Synthesis and Transformation, but later it broadened its scope to logic-based Program Development in general.

The motivating force behind LOPSTR has been a belief that declarative paradigms such as logic programming are better suited to program development tasks than traditional non-declarative ones such as the imperative paradigm. Specification, synthesis, transformation or specialisation, analysis, verification and debugging can all be given logical foundations, thus providing a unifying framework for the whole development process.

In the past ten years or so, such a theoretical framework has indeed begun to emerge. Even tools have been implemented for analysis, verification and specialisation. However, it is fair to say that so far the focus has largely been on programming-in-the-small. So the future challenge is to apply or extend these techniques to programming-in-the-large, in order to tackle software engineering in the real world.

In this paper, we focus on the problem of existence and computing of small and large stable models. We show that for every fixed integer k, there is a linear-time algorithm to decide the problem LSM (large stable models problem): does a logic program P have a stable model of size at least [mid ]P[mid ]−k? In contrast, we show that the problem SSM (small stable models problem) to decide whether a logic program P has a stable model of size at most k is much harder. We present two algorithms for this problem but their running time is given by polynomials of order depending on k. We show that the problem SSM is fixed-parameter intractable by demonstrating that it is W[2]-hard. This result implies that it is unlikely an algorithm exists to compute stable models of size at most k that would run in time O(mc), where m is the size of the program and c is a constant independent of k. We also provide an upper bound on the fixed-parameter complexity of the problem SSM by showing that it belongs to the class W[3].

The paper proposes a new knowledge representation language, called DLP<, which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP< is provided, which is shown to generalize the Answer Set Semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP<. The complexity of DLP< is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP< has exactly the same complexity as reasoning in disjunctive logic programming. This is confirmed by the existence of an efficient translation from DLP< to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP< language has been implemented on top of the DLV system and has subsequently been integrated into DLV.

Existing refinement calculi provide frameworks for the stepwise development of imperative programs from specifications. This paper presents a refinement calculus for deriving logic programs. The calculus contains a wide-spectrum logic programming language, including executable constructs such as sequential conjunction, disjunction, and existential quantification, as well as specification constructs such as general predicates, assumptions and universal quantification. A declarative semantics is defined for this wide-spectrum language based on executions. Executions are partial functions from states to states, where a state is represented as a set of bindings. The semantics is used to define the meaning of programs and specifications, including parameters and recursion. To complete the calculus, a notion of correctness-preserving refinement over programs in the wide-spectrum language is defined and refinement laws for developing programs are introduced. The refinement calculus is illustrated using example derivations and prototype tool support is discussed.

It is important that practical data-flow analyzers are backed by reliably proven theoretical results. Abstract interpretation provides a sound mathematical framework and necessary generic properties for an abstract domain to be well-defined and sound with respect to the concrete semantics. In logic programming, the abstract domain Sharing is a standard choice for sharing analysis for both practical work and further theoretical study. In spite of this, we found that there were no satisfactory proofs for the key properties of commutativity and idempotence that are essential for Sharing to be well-defined and that published statements of the soundness of Sharing assume the occurs-check. This paper provides a generalization of the abstraction function for Sharing that can be applied to any language, with or without the occurs-check. Results for soundness, idempotence and commutativity for abstract unification using this abstraction function are proven.

In this paper, we consider an approach to update nonmonotonic knowledge bases represented as extended logic programs under the answer set semantics. In this approach, new information is incorporated into the current knowledge base subject to a causal rejection principle, which enforces that, in case of conflicts between rules, more recent rules are preferred and older rules are overridden. Such a rejection principle is also exploited in other approaches to update logic programs, notably in the method of dynamic logic programming, due to Alferes et al.

One of the central issues of this paper is a thorough analysis of various properties of the current approach, in order to get a better understanding of the inherent causal rejection principle. For this purpose, we review postulates and principles for update and revision operators which have been proposed in the area of theory change and nonmonotonic reasoning. Moreover, some new properties for approaches to updating logic programs are considered as well. Like related update approaches, the current semantics does not incorporate a notion of minimality of change, so we consider refinements of the semantics in this direction. We also investigate the relationship of our approach to others in more detail. In particular, we show that the current approach is semantically equivalent to inheritance programs, which have been independently defined by Buccafurri et al., and that it coincides with certain classes of dynamic logic programs. In view of this analysis, most of our results about properties of the causal rejection principle apply to each of these approaches as well. Finally, we also deal with computational issues. Besides a discussion on the computational complexity of our approach, we outline how the update semantics and its refinements can be directly implemented on top of existing logic programming systems. In the present case, we implemented the update approach using the logic programming system DLV.

Abstract interpretation is a general methodology for systematic development of program analyses. An abstract interpretation framework is centered around a parametrized non-standard semantics that can be instantiated by various domains to approximate different program properties. Many abstract interpretation frameworks and analyses for Prolog have been proposed, which seek to extract information useful for program optimization. Although motivated by practical considerations, notably making Prolog competitive with imperative languages, such frameworks fail to capture some of the control structures of existing implementations of the language. In this paper, we propose a novel framework for the abstract interpretation of Prolog which handles the depth-first search rule and the cut operator. It relies on the notion of substitution sequence to model the result of the execution of a goal. The framework consists of (i) a denotational concrete semantics, (ii) a safe abstraction of the concrete semantics defined in terms of a class of post-fixpoints, and (iii) a generic abstract interpretation algorithm. We show that traditional abstract domains of substitutions may easily be adapted to the new framework, and provide experimental evidence of the effectiveness of our approach. We also show that previous work on determinacy analysis, that was not expressible by existing abstract interpretation frameworks, can be seen as an instance of our framework. The ideas developed in this paper can be applied to other logic languages, notably to constraint logic languages, and the theoretical approach should be of general interest for the analysis of many non-deterministic programming languages.

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.

Many metainterpreters found in the logic programming literature are nondeterministic in the sense that the selection of program clauses is not determined. Examples are the familiar ‘demo’ and ‘vanilla’ metainterpreters. For some applications this nondeterminism is convenient. In some cases, however, a deterministic metainterpreter, having an explicit selection of clauses, is needed. Such cases include (1) conversion of OR parallelism into AND parallelism for ‘committed-choice’ processors, (2) logic-based, imperative-language implementation of search strategies, and (3) simulation of bounded-resource reasoning. Deterministic metainterpreters are difficult to write because the programmer must be concerned about the set of unifiers of the children of a node in the derivation tree. We argue that it is both possible and advantageous to write these metainterpreters by reasoning in terms of object programs converted into a syntactically restricted form that we call ‘chain’ form, where we can forget about unification, except for unit clauses. We give two transformations converting logic programs into chain form, one for ‘moded’ programs (implicit in two existing exhaustive-traversal methods for committed-choice execution), and one for arbitrary definite programs. As illustrations of our approach we show examples of the three applications mentioned above.

Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a well-automated application of parts of the Burstall-Darlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges.

This paper presents a programming language which includes paradigms that are usually associated with declarative languages, such as sets, rules and search, into an imperative (functional) language. Although these paradigms are separately well known and are available under various programming environments, the originality of the CLAIRE language comes from the tight integration, which yields interesting run-time performances, and from the richness of this combination, which yields new ways in which to express complex algorithmic patterns with few elegant lines. To achieve the opposite goals of a high abstraction level (conciseness and readability) and run-time performance (CLAIRE is used as a C++ preprocessor), we have developed two kinds of compiler: first, a pattern pre-processor handles iterations over both concrete and abstract sets (data types and program fragments), in a completely user-extensible manner; secondly, an inference compiler transforms a set of logical rules into a set of functions (demons that are used through procedural attachment).

An important aspect of data integration involves answering queries using various resources rather than by accessing database relations. The process of transforming a query from the database relations to the resources is often referred to as query folding or answering queries using views, where the views are the resources. We present a uniform approach that includes as special cases much of the previous work on this subject. Our approach is logic-based using resolution. We deal with integrity constraints, negation, and recursion also within this framework.

One recurring problem in program development is that of understanding how to re-use code developed by a third party. In the context of (constraint) logic programming, part of this problem reduces to figuring out how to query a program. If the logic program does not come with any documentation, then the programmer is forced to either experiment with queries in an ad hoc fashion or trace the control-flow of the program (backward) to infer the modes in which a predicate must be called so as to avoid an instantiation error. This paper presents an abstract interpretation scheme that automates the latter technique. The analysis presented in this paper can infer moding properties which if satisfied by the initial query, come with the guarantee that the program and query can never generate any moding or instantiation errors. Other applications of the analysis are discussed. The paper explains how abstract domains with certain computational properties (they condense) can be used to trace control-flow backward (right-to-left) to infer useful properties of initial queries. A correctness argument is presented and an implementation is reported.

Complementation, the inverse of the reduced product operation, is a technique for systematically finding minimal decompositions of abstract domains. Filé and Ranzato advanced the state of the art by introducing a simple method for computing a complement. As an application, they considered the extraction by complementation of the pair-sharing domain PS from the Jacobs and Langen's set-sharing domain SH. However, since the result of this operation was still SH, they concluded that PS was too abstract for this. Here, we show that the source of this result lies not with PS but with SH and, more precisely, with the redundant information contained in SH with respect to ground-dependencies and pair-sharing. In fact, a proper decomposition is obtained if the non-redundant version of SH, PSD, is substituted for SH. To establish the results for PSD, we define a general schema for subdomains of SH that includes PSD and Def as special cases. This sheds new light on the structure of PSD and exposes a natural though unexpected connection between Def and PSD. Moreover, we substantiate the claim that complementation alone is not sufficient to obtain truly minimal decompositions of domains. The right solution to this problem is to first remove redundancies by computing the quotient of the domain with respect to the observable behavior, and only then decompose it by complementation.

Termination of logic programs depends critically on the selection rule, i.e. the rule that determines which atom is selected in each resolution step. In this article, we classify programs (and queries) according to the selection rules for which they terminate. This is a survey and unified view on different approaches in the literature. For each class, we present a sufficient, for most classes even necessary, criterion for determining that a program is in that class. We study six classes: a program strongly terminates if it terminates for all selection rules; a program input terminates if it terminates for selection rules which only select atoms that are sufficiently instantiated in their input positions, so that these arguments do not get instantiated any further by the unification; a program local delay terminates if it terminates for local selection rules which only select atoms that are bounded w.r.t. an appropriate level mapping; a program left-terminates if it terminates for the usual left-to-right selection rule; a program ∃-terminates if there exists a selection rule for which it terminates; finally, a program has bounded nondeterminism if it only has finitely many refutations. We propose a semantics-preserving transformation from programs with bounded nondeterminism into strongly terminating programs. Moreover, by unifying different formalisms and making appropriate assumptions, we are able to establish a formal hierarchy between the different classes.