Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language. Constraint Handling Rules related research has surged during the decade following the previous survey by Frühwirth (J. Logic Programming, Special Issue on Constraint Logic Programming, 1998, vol. 37, nos. 1–3, pp. 95–138). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions, and applications.

The Logic Programming (LP) community, through the Association for Logic Programming (ALP) and its Executive Committee, decided to introduce for 2010 important changes in the way the main yearly results in LP and related areas are published. Whereas such results have appeared to date in standalone volumes of proceedings of the yearly International Conferences on Logic Programming (ICLP), and this method—fully in the tradition of Computer Science (CS)—has served the community well, it was felt that an effort needed to be made to achieve a higher level of compatibility with the publishing mechanisms of other fields outside CS.

This paper presents a logic language for expressing search and optimization problems. Specifically, first a language obtained by extending (positive) DATALOG with intuitive and efficient constructs (namely, stratified negation, constraints, and exclusive disjunction) is introduced. Next, a further restricted language only using a restricted form of disjunction to define (nondeterministically) subsets (or partitions) of relations is investigated. This language, called atalog, captures the power of DATALOG¬ in expressing search and optimization problems. A system prototype implementing atalog is presented. The system translates atalog queries into Optimization Programming Language (OPL) programs which are executed by the ILOG OPL Development Studio. Our proposal combines easy formulation of problems, expressed by means of a declarative logic language, with the efficiency of the ILOG System. Several experiments show the effectiveness of this approach.

Much has happened in data and knowledge base research since the introduction of the relational model in Codd (1970) and its strong logical foundations influence its advances ever since. Logic has been a common ground where Database and Artificial Intelligence research competed and collaborated with each other for a long time (Abiteboul et al. 1995). The product of this joint effort has been a set of logic-based formalisms, such as the Relational Calculus (Codd 1970), Datalog (Ceri et al. 1990), Description Logics (Baader et al. 2007), etc., capturing not only the structure but also the semantics of data in an explicit way, thus enabling complex inference procedures.

We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

Action description languages, such as and ℬ (Gelfond and Lifschitz, Electronic Transactions on Artificial Intelligence, 1998, vol. 2, pp. 193—210), are expressive instruments introduced for formalizing planning domains and planning problem instances. The paper starts by proposing a methodology to encode an action language (with conditional effects and static causal laws), a slight variation of ℬ, using Constraint Logic Programming over Finite Domains. The approach is then generalized to raise the use of constraints to the level of the action language itself. A prototype implementation has been developed, and the preliminary results are presented and discussed.

Termination is an important and well-studied property for logic programs. However, almost all approaches for automated termination analysis focus on definite logic programs, whereas real-world Prolog programs typically use the cut operator. We introduce a novel pre-processing method which automatically transforms Prolog programs into logic programs without cuts, where termination of the cut-free program implies termination of the original program. Hence after this pre-processing, any technique for proving termination of definite logic programs can be applied. We implemented this pre-processing in our termination prover AProVE and evaluated it successfully with extensive experiments.

In the analysis of logic programs, abstract domains for detecting sharing and linearity information are widely used. Devising abstract unification algorithms for such domains has proved to be rather hard. At the moment, the available algorithms are correct but not optimal; i.e., they cannot fully exploit the information conveyed by the abstract domains. In this paper, we define a new (infinite) domain ShLinω which can be thought of as a general framework from which other domains can be easily derived by abstraction. ShLinω makes the interaction between sharing and linearity explicit. We provide a constructive characterization of the optimal abstract unification operator on ShLinω, and we lift it to two well-known abstractions of ShLinω, namely, to the classical Sharing × Lin abstract domain and to the more precise ShLin2 abstract domain by Andy King. In the case of single-binding substitutions, we obtain optimal abstract unification algorithms for such domains.

We consider an extension of logic programs, called ω-programs, that can be used to define predicates over infinite lists. ω-programs allow us to specify properties of the infinite behavior of reactive systems and, in general, properties of infinite sequences of events. The semantics of ω-programs is an extension of the perfect model semantics. We present variants of the familiar unfold/fold rules which can be used for transforming ω-programs. We show that these new rules are correct, that is, their application preserves the perfect model semantics. Then we outline a general methodology based on program transformation for verifying properties of ω-programs. We demonstrate the power of our transformation-based verification methodology by proving some properties of Büchi automata and ω-regular languages.

Head-elementary-set-free (HEF) programs were proposed in (Gebser et al. 2007) and shown to generalize over head-cycle-free programs while retaining their nice properties. It was left as an open problem in (Gebser et al. 2007) to establish the complexity of identifying HEF programs. This note solves the open problem by showing that the problem is complete for coNP.

Since Chen's Entity-Relationship (ER) model, conceptual modeling has been playing a fundamental role in relational data design. In this paper we consider an extended ER (EER) model enriched with cardinality constraints, disjointness assertions, and is a relations among both entities and relationships. In this setting, we consider the case of incomplete data, which is likely to occur, for instance, when data from different sources are integrated. In such a context, we address the problem of providing correct answers to conjunctive queries by reasoning on the schema. Based on previous results about decidability of the problem, we provide a query answering algorithm that performs rewriting of the initial query into a recursive Datalog query encoding the information about the schema. We finally show extensions to more general settings.

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework . We illustrate the application scenarios by means of examples.

One of the differences among the various approaches to suspension-based tabled evaluation is the scheduling strategy. The two most popular strategies are local and batched evaluation. The former collects all the solutions to a tabled predicate before making any one of them available outside the tabled computation. The latter returns answers one by one before computing them all, which in principle is better if only one answer (or a subset of the answers) is desired. Batched evaluation is closer to SLD evaluation in that it computes solutions lazily as they are demanded, but it may need arbitrarily more memory than local evaluation, which is able to reclaim memory sooner. Some programs which in practice can be executed under the local strategy quickly run out of memory under batched evaluation. This has led to the general adoption of local evaluation at the expense of the more depth-first batched strategy. In this paper we study the reasons for the high memory consumption of batched evaluation and propose a new scheduling strategy which we have termed swapping evaluation. Swapping evaluation also returns answers one by one before completing a tabled call, but its memory usage can be orders of magnitude less than batched evaluation. An experimental implementation in the XSB system shows that swapping evaluation is a feasible memory-scalable strategy that need not compromise execution speed.

One of the main advantages of Logic Programming (LP) is that it provides an excellent framework for the parallel execution of programs. In this work we investigate novel techniques to efficiently exploit parallelism from real-world applications in low cost multi-core architectures. To achieve these goals, we revive and redesign the YapOr system to exploit or-parallelism based on a multi-threaded implementation. Our new approach takes full advantage of the state-of-the-art fast and optimized YAP Prolog engine and shares the underlying execution environment, scheduler and most of the data structures used to support YapOr's model. Initial experiments with our new approach consistently achieve almost linear speedups for most of the applications, proving itself as a good alternative for exploiting implicit parallelism in the currently available low cost multi-core architectures.