Abstract
Tabling is a technique of resolution that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant sub-computations. We can distinguish two main categories of tabling mechanisms: suspension-based tabling and linear tabling. In suspension-based tabling, a tabled evaluation can be seen as a sequence of sub-computations that suspend and later resume. Linear tabling mechanisms maintain a single execution tree where tabled subgoals always extend the current computation without requiring suspension and resumption of sub-computations. In this work, we present a new and efficient implementation of linear tabling, but for that we have extended an already existent suspension-based implementation, the YapTab engine. Our design is based on dynamic reordering of alternatives but it innovates by considering a strategy that schedules the re-evaluation of tabled calls in a similar manner to the suspension-based strategies of YapTab. Our implementation also shares the underlying execution environment and most of the data structures used to implement tabling in YapTab. We thus argue that all these common features allows us to make a first and fair comparison between suspension-based and linear tabling and, therefore, better understand the advantages and weaknesses of each.

Abstract
A critical component in the implementation of an efficient tabling system is the design of the data structures and algorithms to access and manipulate tabled data. Arguably, the most successful data structure for tabling is tries, which is regarded as a very compact and efficient; data structure for term representation. Despite these good properties, we found that, for list terms, we can design even more compact and efficient representations. We thus propose a, new representation of list terms for tries that avoids the recursive nature of the WAM representation of list terms in which tries are based. Our experimental results using the YapTab tabling system show a significant reduction in the memory usage for the trie data structures and considerable gains in the running time for storing and loading list terms.

Abstract
(Non-)portability of Prolog programs is widely considered as an important factor in the lack of acceptance of the language. Since 1995, the core of the language is covered by the ISO standard 13211-1. Since 2007, YAP and SWI-Prolog have established a basic compatibility framework. This article describes and evaluates this framework. The aim of the framework is running the same code on both systems rather than migrating an application. We show that today, the portability within the family of Edinburgh/Quintus derived Prolog implementations is good enough to allow for maintaining portable real-world applications.

Abstract
We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks. © 2010 Springer Science+Business Media, LLC.

Abstract
Parallel programming has become mandatory to fully exploit the potential of modern CPUs. The data-flow model provides a natural way to exploit parallelism. However, traditional data-flow programming is not trivial: specifying dependencies and control using fine-grained tasks (such as instructions) can be complex and present unwanted overheads. To address this issue we have built a coarse-grained data-flow model with speculative execution support to be used on top of widespread architectures, implemented as a hybrid Von Neumanm/data-flow execution system. We argue that speculative execution fits naturally with the data-flow model. Using speculative execution liberates the programmer to consider only the main dependencies, and still allows correct data-flow execution of coarse-grained tasks. More- over, our speculation mechanism does not demand centralised control, which is a key feature for upcoming many-core systems, where scalability has become an important concern. An initial study on a artificial bank server application suggests that there is a wide range of scenarios where speculation can be very effective. © 2010 IEEE,.

Abstract
The game of chess has been a major testbed for research in artificial intelligence, since it requires focus on intelligent reasoning. Particularly, several challenges arise to machine learning systems when inducing a model describing legal moves of the chess, including the collection of the examples, the learning of a model correctly representing the official rules of the game, covering all the branches and restrictions of the correct moves, and the comprehensibility of such a model. Besides, the game of chess has inspired the creation of numerous variants, ranging from faster to more challenging or to regional versions of the game. The question arises if it is possible to take advantage of an initial classifier of chess as a starting point to obtain classifiers for the different variants. We approach this problem as an instance of theory revision from examples. The initial classifier of chess is inspired by a FOL theory approved by a chess expert and the examples are defined as sequences of moves within a game. Starting from a standard revision system, we argue that abduction and negation are also required to best address this problem. Experimental results show the effectiveness of our approach.