Abstract
One of the most important advantages of logic programming systems is that they allow the transparent exploitation of parallelism. The different forms of parallelism available and the complex nature of logic programming applications present interesting problems to both the users and the developers of these systems. Graphical visualisation tools can give a particularly important contribution, as they are easier to understand than text based tools, and allow both for a general overview of an execution and for focusing on its important details. Towards these goals, we propose VisAll, anew tool to visualise the parallel execution of logic programs. VisAll benefits from a modular design centered in a graph that represents a parallel execution. A main graphical shell commands the different modules and presents VisAll as an unified system. Several input components, or translators, support the well-known VisAndor and VACE trace formats, plus a new format designed for independent and-parallel plus or-parallel execution in the SEA. Several output components, or visualisers, allow for different visualisations of the same execution.

Abstract
This paper presents an inductive method that learns a logic program represented as an ordered list of clauses. The input consists of a training set of positive examples and background knowledge represented intensionally as a logic program. Our method starts by constructing the explanations of all the positive examples in terms of background knowledge, linking the input to the output arguments. These are used as candidate hypotheses and organized, by relation of generality, into a set of hierarchies (forest). In the second step the candidate hypotheses are analysed with the aim of establishing their effective coverage. In the third step all the inconsistencies are evaluated. This analysis permits to add, at each step, the best hypothesis to the theory. The method was applied to learn the past tense of English verbs. The method presented achieves more accurate results than the previous work by Mooney and Califf [7]. © Springer-Verlag Berlin Heidelberg 1998.

Abstract

Abstract
A subspace-based on-line identification algorithm based on one specific technique, based on Van Overschee and De Moor's results, but can be adapted to other similar methods since they all recover from the state sequence and the observability matrix is presented. These results relate an estimated Kalman filter sequence with an oblique projection. With further improvements, the algorithm can adapt to the identification of time-variant systems.

Abstract
Many state-of-the-art ILP systems require large numbers of negative examples to avoid overgeneralization. This is a considerable disadvantage for many ILP applications, namely inductive program synthesis where relativelly small and sparse example sets are a more realistic scenario. Integrity constraints are first order clauses that can play the role of negative examples in an inductive process. One integrity constraint can replace a long list of ground negative examples. However, checking the consistency of a program with a set of integrity constraints usually involves heavy theorem-proving. We propose an efficient constraint satisfaction algorithm that applies to a wide variety of useful integrity constraints and uses a Monte Carlo strategy. It looks for inconsistencies by random generation of queries to the program. This method allows the use of integrity constraints instead of (or together with) negative examples. As a consequence programs to induce can be specified more rapidly by the user and the ILP system tends to obtain more accurate definitions. Average running times are not greatly affected by the use of integrity constraints compared to ground negative examples.

Abstract