Abstract
In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP( ) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP( ) programs. An implementation of CLP( ) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap . © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
Prolog is traditionally not statically typed. Since the benefits of static typing are huge it was decided to grow a. portable type system inside two widely used open source Prolog systems: SWI-Prolog and Yap. This requires close cooperation and agreement, between the two systems. The type system is Hindley-Milner. The main characteristics of the introduction of types in SWI and Yap are that, typing is not mandatory, that typed and untyped code call be mixed, and that the type checker call insert dynamic type checks at the boundaries between typed and untyped code. The basic decisions and the current status of Hie Typed Prolog project are described. as well as the remaining tasks and problems to be solved.

Abstract
Logic Programming and the Prolog language have a major role in Computing. Prolog, and its derived languages, have been widely used in a impressive variety of application domains. Thus, a bit of the history of Logic Programming reflects in the history of systems such as Dec-10 Prolog [32], M-Prolog [15], C-Prolog [19], Quintus Prolog [20], SICStus Prolog [6], BIM-Prolog [17], ECLiPSe [1], BinProlog [30], SWI-Prolog [34], CIAO [14], and B-Prolog [35], to mention but a few. I briefly present the evolution of one such system, YAP, and present a personal perspective on the challenges ahead for YAP (and for Logic Programming). © 2008 Springer Berlin Heidelberg.

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
One of the major challenges in knowledge discovery is how to extract meaningful and useful knowledge from the complex structured data that one finds in Scientific and Technological applications. One approach is to explore the logic relations in the database and using, say, an Inductive Logic Programming (ILP) algorithm find descriptive and expressive patterns. These patterns can then be used as features to characterize the target concept, The effectiveness of these algorithms depends both upon the algorithm we use to generate the patterns and upon the classifier Rule mining provides an excellent framework for efficiently mining the interesting patterns that are relevant. We propose a novel method to select discriminative patterns and evaluate the effectiveness of this method on a complex discovery application of practical interest.

Abstract
In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on Reinforcement Learning are a good choice to improve the overall performance of grid applications and resource utilization.