Maurice Bruynooghe

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Current literature offers a number of different approaches to what could generally be called “probabilistic logic programming”. These are usually based on Horn clauses. Here, we introduce a new formalism, Logic Programs with Annotated Disjunctions, based on disjunctive logic programs. In this formalism, each of the disjuncts in the head of a clause is(More)
In this paper we develop an algorithm, based on abstract interpretation, for source specialisation of logic programs. This approach is more general than partial evaluation, another technique for source specialisation, and can perform some source specialisations that cannot be done by partial evaluation; examples are specialisations that use information from(More)
This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. 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.(More)
Well-founded orderings are a commonly used tool for proving the termination of programs. We introduce related concepts specialised to SLD-trees. Based on these concepts, we formulate formal and practical criteria for controlling the unfolding during the construction of SLD-trees that form the basis of a partial deduction. We provide algorithms that allow to(More)
Programs for embedded multimedia applications typically manipulate several large multi-dimensional arrays. The energy consumption per access increases with their size; the access to these large arrays is responsible for a substantial part of the power consumption. In this paper, an analysis is developed to compute a bounding box for the elements in the(More)
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 KripkeKleene, the well-founded and the stable semantics for aggregate programs. The semantics is based on(More)
We review Logical Bayesian Networks, a language for probabilistic logical modelling, and discuss its relation to Probabilistic Relational Models and Bayesian Logic Programs. 1 Probabilistic Logical Models Probabilistic logical models are models combining aspects of probability theory with aspects of Logic Programming, first-order logic or relational(More)