Abstract
A critical issue in the design of logic programming systems is their memory performance, both in terms of total memory usage and locality in memory accesses. BEAM, as most modern Prolog systems, requires both good emulator design and good memory performance for best performance. We report on a detailed study of the memory management techniques used on our sequential implementation of the EAM. We address questions like how effective are the techniques the BEAM uses to recover and reuse memory space, how garbage collection affects performance and how to classify and unify variables in a EAM environment. We also propose a finer variable allocation scheme to reduce memory overheads that is quite effective at reducing memory pressure, with only a small overhead.

Abstract
We propose a new approach to Inductive Logic Programming i that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first example of the benefits of our ILP framework, we propose a scheme for denning the hypothesis search space through Inverse Entailment using multiple example seeds. © Springer-Verlag Berlin Heidelberg 2005.

Abstract
Recently, there has been significant work in the integration of probabilistic reasoning with first order logic representations. Learning algorithms for these models have been developed and they all considered modifications in the entire structure. In a previous work we argued that when the theory is approximately correct the use of techniques from theory revision to just modify the structure in places that failed in classification can be a more adequate choice. To score these modifications and choose the best one the log likelihood was used. However, this function was shown not to be well-suited in the propositional Bayesian classification task and instead the conditional log likelihood should be used. In the present paper, we extend this revision system showing the necessity of using specialization operators even when there are no negative examples. Moreover, the results of a theory modified only in places that are responsible for the misclassification of some examples are compared with the one that was modified in the entire structure using three databases and considering four probabilistic score functions, including conditional log likelihood. © Springer-Verlag Berlin Heidelberg 2005.

Abstract
Grid environments are ideal for executing applications that require a huge amount of computational work, both due to the big number of tasks to execute and to the large amount of data to be analysed. Unfortunately, current tools may require that users deal themselves with corrupted outputs or early termination of tasks. This becomes incovenient as the number of parallel runs grows to easily exceed the thousands. ReCS is a user-level software designed to provide automatic detection and restart of corrupted or early terminated tasks. ReGS uses a web interface to allow the setup and control of grid execution, and provides automatic input data setup. ReGS allows the automatic detection of job dependencies, through the GRID-ADL task management language. Our results show that besides automatically and effectively managing a huge number of tasks in grid environments, ReGS is also a good monitoring tool to spot grid nodes pitfalls. © 2005 IEEE.

Abstract
Learning from multi-relational domains has gained increasing attention over the past few years. Inductive logic programming (ILP) systems, which often rely on hill-climbing heuristics in learning first-order concepts, have been a dominating force in the area of multi-relational concept learning. However, hill-climbing heuristics are susceptible to local maxima and plateaus. In this paper, we show how we can exploit the links between objects in multi-relational data to help a first-order rule learning system direct the search by explicitly traversing these links to find paths between variables of interest. Our contributions are twofold: (i) we extend the pathfinding algorithm by Richards and Mooney [12] to make use of mode declarations, which specify the mode of call (input or output) for predicate variables, and (ii) we apply our extended path finding algorithm to saturated bottom clauses, which anchor one end of the search space, allowing us to make use of background knowledge used to build the saturated clause to further direct search. Experimental results on a medium-sized dataset show that path finding allows one to consider interesting clauses that would not easily be found by Aleph. © Springer-Verlag Berlin Heidelberg 2005.

Abstract
Inductive Logic Programming (ILP) is a popular approach for learning rules for classification tasks. An important question is how to combine the individual rules to obtain a useful classifier. In some instances, converting each learned rule into a binary feature for a Bayes net learner improves the accuracy compared to the standard decision list approach [3,4,14]. This results in a two-step process, where rules are generated in the first phase, and the classifier is learned in the second phase. We propose an algorithm that interleaves the two steps, by incrementally building a Bayes net during rule learning. Each candidate rule is introduced into the network, and scored by whether it improves the performance of the classifier. We call the algorithm SAYU for Score As You Use. We evaluate two structure learning algorithms Naïve Bayes and Tree Augmented Naïve Bayes. We test SAYU on four different datasets and see a significant improvement in two out of the four applications. Furthermore, the theories that SAYU learns tend to consist of far fewer rules than the theories in the two-step approach. © Springer-Verlag Berlin Heidelberg 2005.