Titlebook: Probabilistic Inductive Logic Programming; Luc Raedt,Paolo Frasconi,Stephen Muggleton Book 2008 Springer-Verlag Berlin Heidelberg 2008 Bay

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Probabilistic Inductive Logic Programmingintegration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far..In this

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Relational Sequence Learningtly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learnin

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Learning with Kernels and Logical Representationsntation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms. Different representational frameworks and associated algorithms are explored in this chapter. In ., the representation of an exampl

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Markov Logicd relational logic. Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Lea

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CLP(,): Constraint Logic Programming for Probabilistic Knowledgebles, 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