Titlebook: Inductive Logic Programming; 16th International C Stephen Muggleton,Ramon Otero,Alireza Tamaddoni-Ne Conference proceedings 2007 Springer-V

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First-Order Probabilistic Languages: Into the Unknownnomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to ., or ., a

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Integration of Learning and Reasoning Techniquesvariable problems appeared infeasible. Over the last decade, we have witness a qualitative change in the field: current reasoning engines can handle problems with over a million variables and several millions of constraints. I will discuss what led to such a dramatic scale-up, and how progress in re

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Injecting Life with Computersdo with wires and logic gates. In fact, Alan Turing’s notional computer, which marked in 1936 the birth of modern computer science and still stands at its heart, has greater similarity to natural biomolecular machines such as the ribosome and polymerases than to electronic computers. Recently, a new

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On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rateseveral learning algorithms have been conducted on a large set of artificially generated problems by Botta et al. [3]. The authors generated a set of 451 problems by choosing each target concept according to its location in the (.) plane with respect to the PT. The “yes”, “no” and “pt” regions are u

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Inductive Logic Programming for Gene Regulation Predictionl. The efforts are often based on high-throughput genomic data of model organisms such as S. cerevisiae. The goal of this work is to learn a model of gene regulation predicting under which conditions genes are up- or down-regulated. Our starting point is the model of Middendorf . [1], where the pres

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QG/GA: A Stochastic Search for Progolch which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. Initial experiments with QG/GA indicate that this approach can be more eff

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Generalized Ordering-Search for Learning Directed Probabilistic Logical Modelsn proposed. Although many authors provide high-level arguments to show that in principle models in their language can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure an

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