Inductive Logic Programming

This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013.
The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

Inhalt

MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement.- On Differentially Private Inductive Logic Programming.- Learning Through Hypothesis Refinement Using Answer Set Programming.- A BDD-Based Algorithm for Learning from Interpretation Transition.- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization.- A Direct Policy-Search Algorithm for Relational Reinforcement Learning.- AND Parallelism for ILP: The APIS System.- Generalized Counting for Lifted Variable Elimination.- A FOIL-Like Method for Learning under Incompleteness and Vagueness.