Abstract
First-Order Theory Revision from Examples is the process of improving user-defined or automatically generated First-Order Logic (FOL) theories, given a set of examples. So far, the usefulness of Theory Revision systems has been limited by the cost of searching the huge search spaces they generate. This is a general difficulty when learning FOL theories but recent work showed that Stochastic Local Search (SLS) techniques may be effective, at least when learning FOL theories from scratch. Motivated by these results, we propose novel SLS based search strategies for First-Order Theory Revision from Examples. Experimental results show that introducing stochastic search significantly speeds up the runtime performance and improve accuracy.

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.

Abstract
Parallel logic programming (PLP) systems are sophisticated examples of symbolic computing systems. PLP systems address problems such as allocating dynamic memory, scheduling irregular computations, and managing different types of implicit parallelism. Most PLP systems have been developed for bus-based architectures. However, the complexity of PLP systems and the large amount of data they process raise the question of whether logic programming systems can still achieve good performance on modern scalable architectures, such as distributed shared-memory (DSM) systems. In this work we use execution-driven simulation of a cache-coherent DSM architecture to investigate the performance of Andorra-I, a state-of-the-art PLP system, on a modern multiprocessor. The results of this simulation show that Andorra-I exhibits reasonable running time performance, but it does not scale well. Our detailed analysis of cache misses and their sources expose several opportunities for improvements in Andorra-I. Based on this analysis, we modify Andorra-I using a set of simple techniques that led to significantly better running time and scalability. These results suggest that Andorra-I can and should perform well on modern multiprocessors. Furthermore, as Andorra-I shares its main data structures with several PLP systems, we conclude that the methodology and techniques used in our work can greatly benefit these other PLP systems. © 2000 Academic Press.

Abstract
Independent and-parallelism, dependent and-parallelism and or-parallelism are the three main forms of implicit parallelism present in logic programs. In this paper we present a model, IDIOM, which exploits all three forms of parallelism in a single framework. IDIOM is based on a combination of the Basic Andorra Model and the Extended And-Or Tree Model. Our model supports both Prolog as well as the flat concurrent logic languages. We discuss the issues that arise in combining the three forms of parallelism, and our solutions to them. We also present an implementation scheme, based on binding arrays, for implementing IDIOM.

Abstract