Strategies to parallelize ILP systems

It is well known by Inductive Logic Programming (ILP) practionersthat ILP systems usually take a long time to nd valuable models(theories). The problem is specially critical for large datasets, preventingILP systems to scale up to larger applications. One approach to reducethe execution time has been the parallelization of ILP systems. In thispaper we overview the state-of-the-art on parallel ILP implementationsand present work on the evaluation of some major parallelization strategiesfor ILP. Conclusions about the applicability of each strategy arepresented

The 4Cs of adaptation tracking: consistency, comparability, comprehensiveness, coherency

Adaptation tracking seeks to characterize, monitor, and compare general trends in climate change adaptation over time and across nations. Recognized as essential for evaluating adaptation progress, there have been few attempts to develop systematic approaches for tracking adaptation. This is reflected in polarized opinions, contradictory findings, and lack of understanding on the state of adaptation globally. In this paper, we outline key methodological considerations necessary for adaptation tracking research to produce systematic, rigorous, comparable, and usable insights that can capture the current state of adaptation globally, provide the basis for characterizing and evaluating adaptations taking place, facilitate examination of what conditions explain differences in adaptation action across jurisdictions, and can underpin the monitoring of change in adaptation over time. Specifically, we argue that approaches to adaptation tracking need to (i) utilize a consistent and operational conceptualization of adaptation, (ii) focus on comparable units of analysis, (iii) use and develop comprehensive datasets on adaptation action, and (iv) be coherent with our understanding of what constitutes real adaptation. Collectively, these form the 4Cs of adaptation tracking (consistency, comparability, comprehensiveness, and coherency)

Constraint Processing Offers Improved Expressiveness and Inference for Interactive Expert Systems

Abstract. Expert systems constitute one of the most successful application areas for Artificial Intelligence techniques; they have been deployed in many areas of industry and commerce. If-then rules are the core knowledge representation technology in currently deployed systems. However, if we replace rules by constraints, we get improved expressiveness in knowledge representation and richer inference.

Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects

The paper addresses the question how learning class discrimination and learning characteristic class descriptions can be related in relational learning. We present the approach TRITOP/MATCHBOX combining the relational decision tree algorithm TRITOP with the connectionist approach MATCHBOX. TRITOP constructs eciently a relational decision tree for the fast discrimination of classes of relational descriptions, while MATCHBOX is used for constructing class prototypes

A note on two simple transformations for improving the efficiency of an ILP system

Inductive Logic Programming (ILP) systems have had noteworthy successes in extracting comprehensible and accurate models for data drawn from a number of scientifc and engineering domains. These results suggest that ILP methods could enhance the model-construction capabilities of software tools being developed for the emerging discipline of "knowledge discovery from databases." One significant concern in the use of ILP for this purpose is that of efficiency. The performance of modern ILP systems is principally affected by two issues: (1) they often have to search through very large numbers of possible rules (usually in the form of definite clauses); (2) they have to score each rule on the data (usually in the form of ground facts) to estimate "goodness". Stochastic and greedy approaches have been proposed to alleviate the complexity arising from each of these issues. While these techniques can result in order-of-magnitude improvements in the worst-case search complexity of an ..