Titlebook: Inductive Logic Programming; 13th International C Tamás Horváth,Akihiro Yamamoto Conference proceedings 2003 Springer-Verlag Berlin Heidelb

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ILP for Mathematical Discoveryion, deduction, calculation and invention. We describe the HR system which performs a novel ILP routine called automated theory formation. This combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using

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An Exhaustive Matching Procedure for the Improvement of Learning Efficiency as a component of larger systems (e.g., learning systems). Hence, the need of techniques that can speed up such a process. This paper proposes a new algorithm for matching first-order logic descriptions under .-subsumption that is able to return the set of all substitutions by which such a relation

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Graph Kernels and Gaussian Processes for Relational Reinforcement Learningironment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between state-action pairs and their so called Q(uality)-value has to be not only very reliable, but it also has to be able to handle the relational representatio

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A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boostingensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on simple and static methods of determining feature select

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Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms?auses using genetic algorithms. Over the last few years, a few approaches have been proposed aiming to combine genetic and evolutionary computation (EC) with inductive logic programming (ILP). The underlying rationale is that evolutionary algorithms, such as genetic algorithms, might mitigate the co