Abstract
Inductive Logic Programming (ILP) is an established subfield of Machine Learning. Nevertheless, it is recognized that efficiency and scalability is a major obstacle to an increased usage of ILP systems in complex applications with large hypotheses spaces. In this work, we focus on improving the efficiency and scalability of ILP systems by exploring tabling mechanisms available in the underlying Logic Programming systems. Tabling is an implementation technique that improves the declarativeness and performance of Prolog systems by reusing answers to subgoals. To validate our approach, we ran the April ILP system in the YapTab Prolog tabling system using two well-known datasets. The results obtained show quite impressive gains without changing the accuracy and quality of the theories generated.

Abstract
It is well known by Inductive Logic Programming (ILP) practioners that ILP systems usually take a long time to find valuable models (theories). The problem is specially critical for large datasets, preventing ILP systems to scale up to larger applications. One approach to reduce the execution time has been the parallelization of ILP systems. In this paper we overview the state-of-the-art on parallel ILP implementations and present work on the evaluation of some major parallelization strategies for ILP. Conclusions about the applicability of each strategy are presented.

Abstract
This paper aims at evaluate the impact of imitation networks on organizations' survival rates within a Portuguese industrial cluster. We used a Multi-Agent framework to represent the industrial cluster, its firms and the rules underlying the imitation strategies. Several experiments were based on the density dependence model, where vital rates are related with the size of the population (population density). We have concluded that imitation seems to improve the vital dynamics of the population and that present information about a firm is enough to establish an imitation network.

Abstract
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses the shortcomings of previous approaches. A new variant is proposed that exploits, as some previous approaches, meta-learning. The method requires that experiments be conducted on few samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was carried out fully. This in turn permits to generate a prediction regards the relative performance of algorithms. Experimental evaluation shows that the method competes well with previous approaches and provides quite good and practical solution to this problem.

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