We analyze how symmetries can be used to compress structures (also known as interpretations) onto a smaller domain without loss of information. This analysis suggests the possibility to solve satisfiability problems in the compressed domain for better performance. Thus, we propose a 2-step novel method: (i) the sentence to be satisfied is automatically translated into an equisatisfiable sentence over a ``lifted'' vocabulary that allows domain compression; (ii) satisfiability of the lifted sentence is checked by growing the (initially unknown) compressed domain until a satisfying structure is found. The key issue is to ensure that this satisfying structure can always be expanded into an uncompressed structure that satisfies the original sentence to be satisfied. We present an adequate translation for sentences in typed first-order logic extended with aggregates. Our experimental evaluation shows large speedups for generative configuration problems. The method also has applications in the verification of software operating on complex data structures. Our results justify further research in automatic translation of sentences for symmetry reduction.

Abstract Aggregates provide a concise way to express complex knowledge. The problem of selecting an appropriate formalization of aggregates for answer set programming (ASP) remains unsettled. This paper revisits it from the viewpoint of Approximation Fixpoint Theory (AFT). We introduce an AFT formalization equivalent with the Gelfond–Lifschitz reduct for basic ASP programs and we extend it to handle aggregates. We analyze how existing approaches relate to our framework. We hope this work sheds some new light on the issue of a proper formalization of aggregates.

Abstract Symmetry in combinatorial problems is an extensively studied topic. We continue this research in the context of model expansion problems, with the aim of automating the workflow of detecting and breaking symmetry. We focus onlocal domain symmetry, which is induced by permutations of domain elements, and which can be detected on a first-order level. As such, our work is a continuation of the symmetry exploitation techniques of model generation systems, while it differs from more recent symmetry breaking techniques in answer set programming which detect symmetry on ground programs. Our main contributions are sufficient conditions for symmetry of model expansion problems, the identification oflocal domain interchangeability, which can often be broken completely, and efficient symmetry detection algorithms for both local domain interchangeability as well as local domain symmetry in general. Our approach is implemented in the model expansion system IDP, and we present experimental results showcasing the strong and weak points of our approach compared tosbass, a symmetry breaking technique for answer set programming.

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages like ASP and FO(.) and a CSP modeling language such as Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory. An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justifications for the formulas of the non-grounded part, the justifications provide a recipe to construct a complete assignment that satisfies the non-grounded part. When a justification for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justified. Experimental results illustrate the power and generality of this approach.

Abstract This paper provides a gentle introduction to problem-solving with the IDP3 system. The core of IDP3 is a finite model generator that supports first-order logic enriched with types, inductive definitions, aggregates and partial functions. It offers its users a modeling language that is a slight extension of predicate logic and allows them to solve a wide range of search problems. Apart from a small introductory example, applications are selected from problems that arose within machine learning and data mining research. These research areas have recently shown a strong interest in declarative modeling and constraint-solving as opposed to algorithmic approaches. The paper illustrates that the IDP3 system can be a valuable tool for researchers with such an interest. The first problem is in the domain of stemmatology, a domain of philology concerned with the relationship between surviving variant versions of text. The second problem is about a somewhat related problem within biology where phylogenetic trees are used to represent the evolution of species. The third and final problem concerns the classical problem of learning a minimal automaton consistent with a given set of strings. For this last problem, we show that the performance of our solution comes very close to that of the state-of-the art solution. For each of these applications, we analyze the problem, illustrate the development of a logic-based model and explore how alternatives can affect the performance.

Abstract Dynamic systems play a central role in fields such as planning, verification, and databases. Fragmented throughout these fields, we find a multitude of languages to formally specify dynamic systems and a multitude of systems to reason on such specifications. Often, such systems are bound to one specific language and one specific inference task. It is troublesome that performing several inference tasks on the same knowledge requires translations of your specification to other languages. In this paper we study whether it is possible to perform a broad set of well-studied inference tasks on one specification. More concretely, we extend IDP3with several inferences from fields concerned with dynamic specifications.

Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large search spaces containing both decision nodes and probabilistic events. This technique has recently become popular due to its successful application to games, e.g. Poker and Go. Such games have known rules and the alternation between self-moves and non-deterministic events or opponent moves can be used to prune uninteresting branches. In this paper we study a real-world setting where the processes in the domain have a high degree of uncertainty and the need for longer-term planning implies a sequence of (planning) decisions without any intermediate feedback. Fortunately, unlike the combinatorial complexity in strategic games, many real-world environments can be approximated by efficient algorithms on a short term. This paper proposes an MCTS variant using a new type of prior information based on estimating the effects of part of the world and explores its application to the problem of hospital planning, where machine learning algorithms can be used to predict the length of stay of patients for each of the different stages of their recovery.

In Constraint Programming, constraint propagation is a basic component of constraint satisfaction solvers. Here we study constraint propagation as a basic form of inference in the context of first-order logic (FO) and extensions with inductive definitions (FO(ID)) and aggregates (FO(AGG)). In a first, semantic approach, a theory of propagators and constraint propagation is developed for theories in the context of three-valued interpretations. We present an algorithm with polynomial-time data complexity. We show that constraint propagation in this manner can be represented by a datalog program. In a second, symbolic approach, the semantic algorithm is lifted to a constraint propagation algorithm in symbolic structures , symbolic representations of classes of structures. The third part of the article is an overview of existing and potential applications of constraint propagation for model generation, grounding, interactive search problems, approximate methods for ∃∀SO problems, and approximate query answering in incomplete databases.

Abstract Recent work in Answer Set Programming has integrated ideas from Constraint Programming. This has led to a new field called ASP Modulo CSP (CASP), in which the ASP language is enriched with constraint atoms representing constraint satisfaction problems. These constraints have a more compact grounding and are handled by a new generation of search algorithms. However, the burden is on the modeler to exploit these new constructs in his declarative problem specifications. Here, we explore how to remove this burden by automatically generating constraint atoms. We do so in the context of FO(·)IDP, a knowledge representation language that extends first-order logic with, among others, inductive definitions, arithmetic and aggregates. We uncover functional dependencies in declarative problem specifications with a theorem prover and exploit them with a transformation that introduces functions. Experimental evaluation shows that we obtain more compact groundings and better search performance.

This paper considers the fragment ∃∀SO of second-order logic. Many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (ΣP2) and many of these problems are often solved approximately. In this paper, we develop a general approximative method, i.e., a sound but incomplete method, for solving ∃∀SO satisfiability problems. We use a syntactic representation of a constraint propagation method for first-order logic to transform such an ∃∀SO satisfiability problem to an ∃SO(ID) satisfiability problem (second-order logic, extended with inductive definitions). The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. Inductive definitions are a powerful knowledge representation tool, and this moti- vates us to also approximate ∃∀SO(ID) problems. In order to do this, we first show how to perform propagation on such inductive definitions. Next, we use this to approximate ∃∀SO(ID) satisfiability problems. All this provides a general theoretical framework for a number of approximative methods in the literature. Moreover, we also show how we can use this framework for solving practical useful problems, such as conformant planning, in an effective way.

Designers often apply manual or semi-automatic loop and data transformations on array- and loop-intensive programs to improve performance. It is crucial that such transformations preserve the functionality of the program. This article presents an automatic method for constructing equivalence proofs for the class of static affine programs. The equivalence checking is performed on a dependence graph abstraction and uses a new approach based on widening to find the proper induction hypotheses for reasoning about recurrences. Unlike transitive-closure-based approaches, this widening approach can also handle nonuniform recurrences. The implementation is publicly available and is the first of its kind to fully support commutative operations.

Abstract Today, there exist many different probabilistic programming languages as well as more inference mechanisms for these languages. Still, most logic programming-based languages use backward reasoning based on Selective Linear Definite resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. In order to achieve efficiency, we integrate two logic programming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent with the evidence.

We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. The paper defines a semantics for FOProbLog, develops a translation into ProbLog, a system that allows a user to compute the probability of a query in a similar setting restricted to Horn clauses, and reports on initial experience with inference.

The Closed World Assumption (CWA) on databases expresses the assumption that an atom not in the database is false. This assumption is applicable only in cases where the database has complete knowledge about the domain of discourse. In this article, we investigate locally closed databases, that is: databases that are sound but partially incomplete about their domain. Such databases consist of a standard database instance, augmented with a collection of Local Closed World Assumptions (LCWAs). A LCWA is a “local” form of the CWA, expressing that a database relation is complete in a certain area, called a window of expertise . In this work, we study locally closed databases both from a knowledge representation and from a computational perspective. At the representation level, the approach taken in this article distinguishes between the data that is conveyed by a database and the metaknowledge about the area in which the data is complete. We study the semantics of the LCWA's and relate it to several knowledge representation formalisms. At the reasoning level, we study the complexity of, and algorithms for two basic reasoning tasks: computing certain and possible answers to queries and determining whether a database has complete knowledge on a query. As the complexity of these tasks is unacceptably high, we develop efficient approximate methods for query answering. We also prove that for useful classes of queries and locally closed databases, these methods are optimal , and thus they solve the original query in a tractable way. As a result, we obtain classes of queries and locally closed databases for which query answering is tractable.

Abstract This paper develops a logical language for representing probabilistic causal laws. Our interest in such a language is two-fold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

String based as well as tree based methods have been used to learn wrappers for extraction from semi-structured documents (e.g., HTML documents). Previous work has shown that tree based approaches perform better while needing less examples than string based approaches. A disadvantage is that they can only extract complete text nodes, whereas string based approaches can extract within text nodes. This paper proposes a hybrid approach that combines the advantages of both systems and compares it experimentally with a string based approach on some sub node extraction tasks.

This paper presents a novel method to construct a dynamic single assignment (DSA) form of array intensive, pointer free C programs. A program in DSA form does not perform any destructive update of scalars and array elements; that is, each element is written at most once. As DSA makes the dependencies between variable references explicit, it facilitates complex analyses and optimizations of programs. Existing transformations into DSA perform a complex data flow analysis with exponential analysis time, and they work only for a limited class of input programs. Our method removes irregularities from the data flow by adding copy assignments to the program, so that it can use simple data flow analyses. The presented DSA transformation scales very well with growing program sizes and overcomes a number of important limitations of existing methods. We have implemented the method and it is being used in the context of memory optimization and verification of those optimizations. Experiments show that in practice, the method scales well indeed, and that added copy operations can be removed in case they are unwanted.

This article makes two contributions to the work on semantics-based termination analysis for logic programs. The first involves a novel notion of type - based norm where for a given type, a corresponding norm is defined to count in a term the number of subterms of that type. This provides a collection of candidate norms, one for each type defined in the program. The second enables an analyzer to base termination proofs on the combination of several different norms. This is useful when different norms are better suited to justify the termination of different parts of the program. Application of the two contributions together consists in considering the combination of the type-based candidate norms for a given program. This results in a powerful and practical technique. Both contributions have been introduced into a working termination analyzer. Experimentation indicates that they yield state-of-the-art results in a fully automatic analysis tool, improving with respect to methods that do not use both types and combined norms.

Abstract In this paper, we present a framework for the semantics and the computation of aggregates in the context of logic programming. In our study, an aggregate can be an arbitrary interpreted second order predicate or function. We define extensions of the Kripke-Kleene, the well-founded and the stable semantics for aggregate programs. The semantics is based on the concept of a three-valued immediate consequence operator of an aggregate program. Such an operator approximates the standard two-valued immediate consequence operator of the program, and induces a unique Kripke-Kleene model, a unique well-founded model and a collection of stable models. We study different ways of defining such operators and thus obtain a framework of semantics, offering different trade-offs between precision and tractability . In particular, we investigate conditions on the operator that guarantee that the computation of the three types of semantics remains on the same level as for logic programs without aggregates. Other results show that, in practice, even efficient three-valued immediate consequence operators which are very low in the precision hierarchy, still provide optimal precision.

One of the disadvantages of statically typed languages is the programming overhead caused by writing all the necessary type information: Both type declarations and type definitions are typically required. Traditional type inference aims at relieving the programmer from the former.We present a rule-based constraint rewriting algorithm that reconstructs both type declarations and type definitions, allowing the programmer to effectively program type-less in a strictly typed language. This effectively combines strong points of dynamically typed languages (rapid prototyping) and statically typed ones (documentation, optimized compilation). Moreover it allows to quickly port code from a statically untyped to a statically typed setting.Our constraint-based algorithm reconstructs uniform polymorphic definitions of algebraic data types and simultaneously infers the types of all expressions and functions (supporting polymorphic recursion) in the program. The declarative nature of the algorithm allows us to easily show that it has a number of highly desirable properties such as soundness, completeness and various optimality properties. Moreover, we show how to easily extend and adapt it to suit a number of different language constructs and type system features

The development of energy and performance-efficient embedded software increasingly relies on the application of complex transformations on critical parts of the source code. Designers applying such nontrivial source code transformations are often faced with the problem of ensuring functional equivalence of the original and transformed programs. Currently, they have to rely on incomplete and time-consuming simulation. Formal automatic verification of the transformed program against the original is desirable instead. This calls for equivalence checking tools similar to the ones available for comparing digital circuits. We present such a tool to compare array-intensive programs related through a combination of important global transformations like expression propagations, loop and algebraic transformations. When the transformed program fails to pass the equivalence check, the tool provides specific feedback on the possible locations of errors.

Many optimization techniques, including several targeted specifically at embedded systems, depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric polytopes. It is well known that this parametric count can be represented by a set of Ehrhart polynomials. Previously, interpolation was used to obtain these polynomials, but this technique has several disadvantages. Its worst-case computation time for a single Ehrhart polynomial is exponential in the input size, even for fixed dimensions. The worst-case size of such an Ehrhart polynomial (measured in bits needed to represent the polynomial) is also exponential in the input size. Under certain conditions this technique even fails to produce a solution.Our main contribution is a novel method for calculating Ehrhart polynomials analytically. It extends an existing method, based on Barvinok's decomposition, for counting the number of integer points in a non-parametric polytope. Our technique always produces a solution and computes polynomially-sized Ehrhart polynomials in polynomial time (for fixed dimensions).

Abstract: We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases.

This paper illustrates how a Prolog program, using chronological backtracking to find a solution in some search space, can be enhanced to perform intelligent backtracking. The enhancement crucially relies on the impurity of Prolog that allows a program to store information when a dead end is reached. To illustrate the technique, a simple search program is enhanced.

The so called “cogen approach” to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that the cogen approach is also applicable to the specialisation of logic programs (called partial deduction) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system LIX for pure logic programs. While this is a usable specialiser by itself, it is used to develop the cogen system LOGEN. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, LOGEN generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the LOGEN system in a fully automated way. Finally, LOGEN is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisations.

Affine loop transformations have often been used for program optimization. Usually their focus lies on single loop nests. A few recent approaches also handle global programs with multiple loop nests but they are not really scalable towards realistic applications with dozens of nests. To reduce complexity, we split affine transformations into a linear transformation step and a translation step. This translation step can be used to perform general multidimensional loop fusion. We show that loop fusion can be performed incrementally and provide a greedy algorithm, which we illustrate on a simple example. Finally, we present a heuristic for data locality and provide some experimental results.

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.

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.

Logic programming has been introduced as programming in the Horn clause subset of first-order logic. This view breaks down for the negation as failure inference rule. To overcome the problem, one line of research has been to view a logic program as a set of iff-definitions. A second approach was to identify a unique canonical, preferred , or intended model among the models of the program and to appeal to common sense to validate the choice of such model. Another line of research developed the view of logic programming as a nonmonotonic reasoning formalism strongly related to Default Logic and Autoepistemic Logic. These competing approaches have resulted in some confusion about the declarative meaning of logic programming. This paper investigates the problem and proposes an alternative epistemological foundation for the canonical model approach, which is not based on common sense but on a solid mathematical information principle. The thesis is developed that logic programming can be understood as a natural and general logic of inductive definitions . In particular, logic programs with negation represent nonmonotone inductive definitions . It is argued that this thesis results in an alternative justification of the well-founded model as the unique intended model of the logic program. In addition, it equips logic programs with an easy-to-comprehend meaning that corresponds very well with the intuitions of programmers.

The natural representation of solutions of finite constraint satisfaction problems is as a (set of) function(s) or relation(s). In (constraint) logic programming, answers are in the form of substitutions to the variables in the query. This results in a not very declarative programming style where a table has to be presented as a complex term. Recently, stable logic programming, also called answer set programming and abductive logic programming have been proposed as approaches supporting a more declarative style for solving such problems. The approach developed in this paper is to extend the constraint domain of a constraint logic programming language with open functions, functions for which the interpretation is not fixed in advance. Their interpretation contains the solution of the problem. This enrichment of the constraint domain yields a language which is almost as expressive as abductive logic programming and is very well suited for expressing finite domain constraint satisfaction problems. Implementation requires only to extend the constraint solver of the underlying CLP language.

This article presents and illustrates a practical approach to the dataflow analysis of constraint logic programming languages using abstract interpretation. It is first argued that, from the framework point of view, it suffices to propose relatively simple extensions of traditional analysis methods which have already been proved useful and practical and for which efficient fixpoint algorithms exist. This is shown by proposing a simple extension of Bruynooghe's traditional framework which allows it to analyze constraint logic programs. Then, and using this generalized framework, two abstract domains and their required abstract functions are presented: the first abstract domain approximates definiteness information and the second one freeness. Finally, an approach for combining those domains is proposed. The two domains and their combination have been implemented and used in the analysis of CLP( R ) and Prolog-III applications. Results form this implementation showing its performance and accuracy are also presented.

This article presents the theory and implementation of an artificial intelligence planner, CHICA. CHICA is a non-linear, domain independent planner based on techniques of computational logic. The representation language of the planner is Horn clause logic which is used to model event calculus, a logical theory of changing properties over time. The reasoning component is an abductive extension of SLDNF resolution for generating assumptions to prove a given goal. In event calculus, this procedure generates a plan of events and temporal relations necessary to prove the planning goal. CHICA uses domain contraints and techniques from contraint logic programming to efficiently implement inequality, as well as a specialized module to evaluate temporal relations. CHICA's generic search algorithm lets the implementor of a planning domain define a particular search strategy and specify domain heuristics to prune the search space. CHICA has solved a number of planning problems successfully: multiple robot block world problems, the assembly of a flashlight, and a room decoration problem. Extensions to classical Al-planning can be solved within the same framework, such as plan execution and replanning.

This article considers static analysis based on abstract interpretation of logic programs over combined domains. It is known that analyses over combined domains provide more information potentially than obtained by the independent analyses. However, the construction of a combined analysis often requires redefining the basic operations for the combined domain. A practical approach to maintain precision in combined analyses of logic programs which reuses the individual analyses and does not redefine the basic operations is illustrated. The advantages of the approach are that (1) proofs of correctness for the new domains are not required and (2) implementations can be reused. The approach is demonstrated by showing that a combined sharing analysis—constructed from “old” proposals—compares well with other “new” proposals suggested in recent literature both from the point of view of efficiency and accuracy.

For the class of applicative programming languages, efficient methods for reclaiming the memory occupied by released data structures constitute an important aspect of current implementations. The present article addresses the problem of memory reuse for logic programs through program analysis rather than by run-time garbage collection. The aim is to derive run-time properties that can be used at compile time to specialize the target code for a program according to a given set of queries and to automatically introduce destructive assignments in a safe and transparent way so that fewer garbage cells are created. The dataflow analysis is constructed as an application of abstract interpretation for logic programs. An abstract domain for describing structure-sharing and liveness properties is developed as are primitive operations that guarantee a sound and terminating global analysis. We explain our motivation for the design of the abstract domain, make explicit the underlying implementation assumptions, and discuss the precision of the results obtained by a prototype analyzer.

In this paper we consider static analyses based on abstract interpretation of logic programs over combined domains. It is known that analyses over combined domains potentially provide more information than obtainable by performing the independent abstract interpretations. However, the construction of a combined analysis often requires redefining the basic operations for the combined domain. We demonstrate for logic programs that in practice it is possible to obtain precision in a combined analysis without redefining the basic operations. We also propose a way of performing the combination which can be more precise than the straightforward application of the classical “reduced product” approach, while keeping the original components of the basic operations. The advantage of the approach is that proofs of correctness for the new domains are not required and implementations can be reused. We illustrate our results by showing that a combined sharing analysis—constructed from “old” proposals—compares well with other “new” proposals suggested in recent literature both from the point of view of efficiency and accuracy.

A novel approach to interactively acquire knowledge about new objects in a logic environment is presented. When the user supplies an unknown fact containing unknown objects (constants), the system will ask interesting membership and existential queries about the objects. The answers to these questions allow the system to update its knowledge base. Two basic strategies are implemented: one that examines existing Horn clauses for the predicate and another one that uses types. Furthermore, a powerful heuristic based on analogy, to pose the most interesting questions first, is presented.