Program transformation is an appealing technique which allows to improve run-time efficiency, space-consumption, and more generally to optimize a given program. Essentially, it consists of a sequence of syntactic program manipulations which preserves some kind of semantic equivalence. Unfolding is one of the basic operations used by most program transformation systems and consists of the replacement of a procedure call by its definition. While there is a large body of literature on the transformation and unfolding of sequential programs, very few papers have addressed this issue for concurrent languages. This paper defines an unfolding system for Constraint Handling Rules programs. We define an unfolding rule, show its correctness and discuss some conditions that can be used to delete an unfolded rule while preserving the program meaning. We also prove that, under some suitable conditions, confluence and termination are preserved by the above transformation.

Timed Concurrent Constraint Programming (tcc) is a declarative model for concurrency offering a logic for specifying reactive systems, i.e., systems that continuously interact with the environment. The universal tcc formalism (utcc) is an extension of tcc with the ability to express mobility. Here mobility is understood as communication of private names as typically done for mobile systems and security protocols. In this paper we consider the denotational semantics for tcc, and extend it to a “collecting” semantics for utcc based on closure operators over sequences of constraints. Relying on this semantics, we formalize a general framework for data flow analyses of tcc and utcc programs by abstract interpretation techniques. The concrete and abstract semantics that we propose are compositional, thus allowing us to reduce the complexity of data flow analyses. We show that our method is sound and parametric with respect to the abstract domain. Thus, different analyses can be performed by instantiating the framework. We illustrate how it is possible to reuse abstract domains previously defined for logic programming to perform, for instance, a groundness analysis for tcc programs. We show the applicability of this analysis in the context of reactive systems. Furthermore, we also make use of the abstract semantics to exhibit a secrecy flaw in a security protocol. We also show how it is possible to make an analysis which may show that tcc programs are suspension-free. This can be useful for several purposes, such as for optimizing compilation or for debugging.

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

There are many different semantics for general logic programs (i.e. programs that use negation in the bodies of clauses). Most of these semantics are Turing complete (in a sense that can be made precise), implying that they are undecidable. To obtain decidability one needs to put additional restrictions on programs and queries. In logic programming it is natural to put restrictions on the underlying first-order language. In this note, we show the decidability of the Clark's completion semantics for monadic general programs and queries.