Estimation-Based Search Space Traversal in PILP Environments

Joana Côrte-Real, Inês Dutra and Ricardo Rocha

September 2016


Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy.


  author =    {J. Côrte-Real and I. Dutra and R. Rocha},
  title =     {{Estimation-Based Search Space Traversal in PILP Environments}},
  booktitle = {Proceedings of the 26th International Conference on Inductive Logic Programming (ILP 2016)},
  pages =     {1--13},
  number =    {10326},
  series =    {LNAI},
  publisher = {Springer},
  editor =    {A. Russo and J. Cussens},
  month =     {September},
  year =      {2016},
  address =   {London, UK},
  note =      {Published in 2017},

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