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.

A PROLOG compiler specializes the code for unification between calls and clause heads as they appear in the program. This code could be further specialized, yielding more efficient code, if more precise information about possible values for actual arguments were available. This paper addresses the problem of gathering such information. It develops a method for obtaining descriptions of possible values of program variables. The method is based upon a framework for abstract interpretation. The descriptions can be regarded as extended modes or a kind of type information. An important issue in the method is the treatment of free variables and the sharing of free variables between different values of program variables. a * Supported by project RFO-AI-02: "Logic as a basis for artificial intelligence: control and efficiency of c$cductive interference-parallelism." Supported by the Belgian National Fund for Scientific Research.

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 array that are simultaneously in use. The size of the original array can be reduced to the size of the bounding box and accesses to it can be redirected using modulo operations on the original indices. This substantially reduces the size of the memories and the power consumption of accessing them.

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.

Designers often apply manual or semi-automatic loop and data transformations on array and loop intensive programs to improve performance. The transformations should preserve the functionality, however, and this paper presents an automatic method to proof equivalence 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 handle recurrences. In contrast to transitive closure based approaches, this widening approach does not require uniform recurrences. The implementation is publicly available and is the first of its kind to fully support commutative operations.