Abstraction-Carrying Code (ACC) has recently been proposed as a framework for mobile code safety in which the code supplier provides a program together with an abstraction (or abstract model of the program) whose validity entails compliance with a predefined safety policy. The abstraction plays thus the role of safety certificate and its generation is carried out automatically by a fixpoint analyzer. The advantage of providing a (fixpoint) abstraction to the code consumer is that its validity is checked in a single pass (i.e., one iteration) of an abstract interpretation-based checker. A main challenge to make ACC useful in practice is to reduce the size of certificates as much as possible while at the same time not increasing checking time. The intuitive idea is to only include in the certificate information that the checker is unable to reproduce without iterating. We introduce the notion of reduced certificate which characterizes the subset of the abstraction which a checker needs in order to validate (and re-construct) the full certificate in a single pass. Based on this notion, we instrument a generic analysis algorithm with the necessary extensions in order to identify the information relevant to the checker. Interestingly, the fact that the reduced certificate omits (parts of) the abstraction has implications in the design of the checker. We provide the sufficient conditions which allow us to ensure that (1) if the checker succeeds in validating the certificate, then the certificate is valid for the program (correctness) and (2) the checker will succeed for any reduced certificate which is valid (completeness). Our approach has been implemented and benchmarked within the CiaoPP system. The experimental results show that our proposal is able to greatly reduce the size of certificates in practice.

In recent years, answer set programming (ASP) has been extended to deal with multivalued predicates. The resulting formalismsallow for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining thestable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where manyefficient solvers have been constructed, to date there is no efficient fuzzy ASP solver. A well-knowntechnique for classical ASP consists of translating an ASP program P to a propositional theory whose models exactlycorrespond to the answer sets of P. In this paper, we show how this idea can be extended to fuzzy ASP, paving the wayto implement efficient fuzzy ASP solvers that can take advantage of existing fuzzy logic reasoners.

It has now been 40 years since the birth of the Prolog language and of its first implementation by A. Colmerauer and P. Roussel. Since then, a large number of Prolog systems have been implemented. While the core of the Prolog language has not changed much in these 40 years, Prolog systems have undergone an extraordinary evolution that stems from two main sources. One is the trend to extend Prolog to incorporate ideas from other language paradigms that have proved useful in real-world applications. This includes concurrency, parallelism, higher order predicates, object-oriented programming, Web interfaces, processing of large amounts of data, and flexible developer tools that enhance reliability and robustness through assertions. A second source of change is the exploration of ideas for which Prolog systems are uniquely suitable and that have led to the creation of new programming paradigms. This includes tabling, constraint logic programming, answer set programming, and probabilistic logic programming.

We are proud to introduce this special issue of the Journal of Theory and Practice of Logic Programming (TPLP), dedicated to the full papers accepted for the 28th International Conference on Logic Programming (ICLP). The ICLP meetings started in Marseille in 1982 and since then constitute the main venue for presenting and discussing work in the area of logic programming.

Multi-threading is currently supported by several well-known Prolog systems providing a highly portable solution for applications that can benefit from concurrency. When multi-threading is combined with tabling, we can exploit the power of higher procedural control and declarative semantics. However, despite the availability of both threads and tabling in some Prolog systems, the implementation of these two features implies complex ties to each other and to the underlying engine. Until now, XSB was the only Prolog system combining multi-threading with tabling. In XSB, tables may be either private or shared between threads. While thread-private tables are easier to implement, shared tables have all the associated issues of locking, synchronization and potential deadlocks. In this paper, we propose an alternative view to XSB's approach. In our proposal, each thread views its tables as private but, at the engine level, we use a common table space where tables are shared among all threads. We present three designs for our common table space approach: No-Sharing (NS) (similar to XSB's private tables), Subgoal-Sharing (SS) and Full-Sharing (FS). The primary goal of this work was to reduce the memory usage for the table space but, our experimental results, using the YapTab tabling system with a local evaluation strategy, show that we can also achieve significant reductions on running time.

Logic programming provides a high-level view of programming, giving implementers a vast latitude into what techniques to explore to achieve the best performance for logic programs. Towards obtaining maximum performance, one of the holy grails of logic programming has been to design computational models that could be executed efficiently and that would allow both for a reduction of the search space and for exploiting all the available parallelism in the application. These goals have motivated the design of the Extended Andorra Model (EAM), a model where goals that do not constrain nondeterministic goals can execute first. In this work, we present and evaluate the Basic design for EAM, a system that builds upon David H. D. Warren's original EAM with Implicit Control. We provide a complete description and implementation of the Basic design for EAM System as a set of rewrite and control rules. We present the major data structures and execution algorithms that are required for efficient execution, and evaluate system performance. A detailed performance study of our system is included. Our results show that the system achieves acceptable base performance and that a number of applications benefit from the advanced search inherent to the EAM.

Yet Another Prolog (YAP) is a Prolog system originally developed in the mid-eighties and that has been under almost constant development since then. This paper presents the general structure and design of the YAP system, focusing on three important contributions to the Logic Programming community. First, it describes the main techniques used in YAP to achieve an efficient Prolog engine. Second, most Logic Programming systems have a rather limited indexing algorithm. YAP contributes to this area by providing a dynamic indexing mechanism, or just-in-time indexer. Third, a important contribution of the YAP system has been the integration of both or-parallelism and tabling in a single Logic Programming system.

Logic programming has developed as a rich field, built over a logical substratum whose main constituent is a nonclassical form of negation, sometimes coexisting with classical negation. The field has seen the advent of a number of alternative semantics, with Kripke–Kleene semantics, the well-founded semantics, the stable model semantics, and the answer-set semantics standing out as the most successful. We show that all aforementioned semantics are particular cases of a generic semantics, in a framework where classical negation is the unique form of negation and where the literals in the bodies of the rules can be ‘marked’ to indicate that they can be the targets of hypotheses. A particular semantics then amounts to choosing a particular marking scheme and choosing a particular set of hypotheses. When a literal belongs to the chosen set of hypotheses, all marked occurrences of that literal in the body of a rule are assumed to be true, whereas the occurrences of that literal that have not been marked in the body of the rule are to be derived in order to contribute to the firing of the rule. Hence the notion of hypothetical reasoning that is presented in this framework is not based on making global assumptions, but more subtly on making local, contextual assumptions, taking effect as indicated by the chosen marking scheme on the basis of the chosen set of hypotheses. Our approach offers a unified view on the various semantics proposed in logic programming, classical in that only classical negation is used, and links the semantics of logic programs to mechanisms that endow rule-based systems with the power to harness hypothetical reasoning.

Coding standards and good practices are fundamental to a disciplined approach to software projects irrespective of programing languages being employed. Prolog programing can benefit from such an approach, perhaps more than programing in other languages. Despite this, no widely accepted standards and practices seem to have emerged till now. The present paper is a first step toward filling this void: It provides immediate guidelines for code layout, naming conventions, documentation, proper use of Prolog features, program development, debugging, and testing. Presented with each guideline is its rationale and, where sensible options exist, illustrations of the relative pros and cons for each alternative. A coding standard should always be selected on a per-project basis, based on a host of issues pertinent to any given programing project; for this reason the paper goes beyond the mere provision of normative guidelines by discussing key factors and important criteria that should be taken into account when deciding on a full-fledged coding standard for the project.

In this work, we propose Answer-Set Programming (ASP) as a tool for rapid prototyping of dynamic programming algorithms based on tree decompositions. In fact, many such algorithms have been designed, but only a few of them found their way into implementation. The main obstacle is the lack of easy-to-use systems which (i) take care of building a tree decomposition and (ii) provide an interface for declarative specifications of dynamic programming algorithms. In this paper, we present D-FLAT, a novel tool that relieves the user of having to handle all the technical details concerned with parsing, tree decomposition, the handling of data structures, etc. Instead, it is only the dynamic programming algorithm itself which has to be specified in the ASP language. D-FLAT employs an ASP solver in order to compute the local solutions in the dynamic programming algorithm. In the paper, we give a few examples illustrating the use of D-FLAT and describe the main features of the system. Moreover, we report experiments which show that ASP-based D-FLAT encodings for some problems outperform monolithic ASP encodings on instances of small treewidth.

SICStus Prolog has evolved for nearly 25 years. This is an appropriate point in time for revisiting the main language and design decisions, and try to distill some lessons. SICStus Prolog was conceived in a context of multiple, conflicting Prolog dialect camps and a fledgling standardization effort. We reflect on the impact of this effort and role model implementations on our development. After summarizing the development history, we give a guided tour of the system anatomy, exposing some designs that were not published before. We give an overview of our new interactive development environment, and describe a sample of key applications. Finally, we try to identify key good and not so good design decisions.

The seaport of Gioia Tauro is the largest transshipment terminal of the Mediterranean coast. A crucial management task for the companies operating in the seaport is team-building: the problem of properly allocating the available personnel for serving the incoming ships. Teams have to be carefully arranged in order to meet several constraints, such as allocation of employees with appropriate skills, fair distribution of the working load, and turnover of the heavy/dangerous roles. This makes team-building a hard and expensive task requiring several hours of manual preparation per day.

In this paper we present a system based on Answer Set Programming for the automatic generation of the teams of employees in the seaport of Gioia Tauro. The system is currently exploited in the Gioia Tauro seaport by ICO BLG, a company specialized in automobile logistics.

SWI-Prolog is neither a commercial Prolog system nor a purely academic enterprise, but increasingly a community project. The core system has been shaped to its current form while being used as a tool for building research prototypes, primarily for knowledge-intensive and interactive systems. Community contributions have added several interfaces and the constraint (CLP) libraries. Commercial involvement has created the initial garbage collector, added several interfaces and two development tools: PlDoc (a literate programming documentation system) and PlUnit (a unit testing environment).

In this article, we present SWI-Prolog as an integrating tool, supporting a wide range of ideas developed in the Prolog community and acting as glue between foreign resources. This article itself is the glue between technical articles on SWI-Prolog, providing context and experience in applying them over a longer period.

We present BEE, a compiler which enables to encode finite domain constraint problems to CNF. Using BEE both eases the encoding process for the user and also performs transformations to simplify constraints and optimize their encoding to CNF. These optimizations are based primarily on equi-propagation and on partial evaluation, and also on the idea that a given constraint may have various possible CNF encodings. Often, the better encoding choice is made after constraint simplification. BEE is written in Prolog and integrates directly with a SAT solver through a suitable Prolog interface. We demonstrate that constraint simplification is often highly beneficial when solving hard finite domain constraint problems. A BEE implementation is available with this paper.

The paper presents a simple and concise proof of correctness of the magic transformation. We believe that it may provide a useful example of formal reasoning about logic programs. The correctness property concerns the declarative semantics. The proof, however, refers to the operational semantics (LD-resolution) of the source programs. Its conciseness is due to applying a suitable proof method.

Nonmonotonic causal logic, introduced by McCain and Turner (McCain, N. and Turner, H. 1997. Causal theories of action and change. In Proceedings of National Conference on Artificial Intelligence (AAAI), Stanford, CA, 460–465) became the basis for the semantics of several expressive action languages. McCain's embedding of definite propositional causal theories into logic programming paved the way to the use of answer set solvers for answering queries about actions described in such languages. In this paper we extend this embedding to nondefinite theories and to the first-order causal logic.

We describe the BinProlog system's compilation technology, runtime system and its extensions supporting first-class Logic Engines while providing a short history of its development, details of some of its newer re-implementations as well as an overview of the most important architectural choices involved in their design. With focus on its differences with conventional Warren Abstract Machine (WAM) implementations, we explain key details of BinProlog's compilation technique, which replaces the WAM with a simplified continuation passing runtime system (the “BinWAM”), based on a mapping of full Prolog to binary logic programs. This is followed by a description of a term compression technique using a “tag-on-data” representation. Later derivatives, the Java-based Jinni Prolog compiler and the recently developed Lean Prolog system refine the BinProlog architecture with first-class Logic Engines, made generic through the use of an Interactor interface. An overview of their applications with focus on the ability to express at source level a wide variety of Prolog built-ins and extensions covers these newer developments.

We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude.