Inductive Logic Programming

1. Front Matter

   Pages I-XIII

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2. MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement

   • Stephen H. Muggleton, Dianhuan Lin, Jianzhong Chen, Alireza Tamaddoni-Nezhad

   Pages 1-17

3. On Differentially Private Inductive Logic Programming

   • Chen Zeng, Eric Lantz, Jeffrey F. Naughton, David Page

   Pages 18-30

4. Learning Through Hypothesis Refinement Using Answer Set Programming

   • Duangtida Athakravi, Domenico Corapi, Krysia Broda, Alessandra Russo

   Pages 31-46

5. A BDD-Based Algorithm for Learning from Interpretation Transition

   • Tony Ribeiro, Katsumi Inoue, Chiaki Sakama

   Pages 47-63

6. Accelerating Imitation Learning in Relational Domains via Transfer by Initialization

   • Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting, Prasad Tadepalli

   Pages 64-75

7. A Direct Policy-Search Algorithm for Relational Reinforcement Learning

   • Samuel Sarjant, Bernhard Pfahringer, Kurt Driessens, Tony Smith

   Pages 76-92

8. AND Parallelism for ILP: The APIS System

   • Rui Camacho, Ruy Ramos, Nuno A. Fonseca

   Pages 93-106

9. Generalized Counting for Lifted Variable Elimination

   • Nima Taghipour, Jesse Davis, Hendrik Blockeel

   Pages 107-122

10. A FOIL-Like Method for Learning under Incompleteness and Vagueness

    • Francesca A. Lisi, Umberto Straccia

    Pages 123-139

11. Back Matter

    Pages 141-141

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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.

• Markov decision process
• algorithms
• answer set programming
• artificial intelligence
• data mining
• inductive logic programming
• logic programming
• logical learning
• machine learning
• multi-relational learning
• probabilistic reasoning
• relational learning
• robotics
• structured data

• University of Rio de Janeiro, Rio de Janeiro, Brazil

  Gerson Zaverucha

• University of Porto, Porto, Portugal

  Vítor Santos Costa

• Fluminense Federal University, Niterói, Brazil

  Aline Paes

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