Titlebook: Inductive Logic Programming; 21st International C Stephen H. Muggleton,Alireza Tamaddoni-Nezhad,Fran Conference proceedings 2012 Springer-V

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Inference and Learning in Planningmputational, as all models, whether accommodating non-determinism and feedback or not, are intractable in the worst case. In the last few years, however, significant progress has been made resulting in algorithms that can produce plans effectively in a variety of settings. These developments have to

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Exploiting Constraintsmall primitives and these decompositions can simulate complex propagation algorithms that perform sophisticated inference about a problem. We illustrate this approach with examples of exploiting constraints in propositional satisfiability (SAT), pseudo-Boolean (PB) solving, integer linear programmin

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Learning Directed Relational Models with Recursive DependenciesA key characteristic of relational data is that the value of a predicate often depends on values of the same predicate for related entities. In this paper we present a new approach to learning directed relational models which utilizes two key concepts: a pseudo likelihood measure that is well define

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