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# Welcome to Pascal

PASCAL is a system for learning probabilistic integrity constraints, see

Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti, and Evelina Lamma. Probabilistic inductive constraint logic. Machine Learning, 110:723–754, 2021. 
[doi:10.1007/s10994-020-05911-6](https://doi.org/10.1007/s10994-020-05911-6)

and help at https://friguzzi.github.io/pascal

This notebook gives an overview of example programs for learning with PASCAL:

  - Bongard ([bongardkeys.pl](example/pascal/bongardkeys.pl), [bongardkeys_rule.pl](example/pascal/bongardkeys_rule.pl), parameter and structure learning) 
  The task is to classify pictures containing geometrical objects. 
  From L. De Raedt and W. Van Laer. _Inductive constraint logic_. In Proceedings of the Sixth International Workshop on Algorithmic Learning Theory, 1995. 
  Both parameters and structure can be learned. The input theory for parameter 
  learning has been manually crafted. Both files contain the examples in 
  the keys format. They differ because in the first the initial theory is given using constraints encoded as strings
  while in the latter the initial theory is given using constraints encoded as logic facts.
  - BUPA ([bupa_d.pl](example/pascal/bupa_d.pl), parameter and structure learning) 
  A medical dataset for diagnosing liver disorders. From
  McDermott and Forsyth, _Diagnosing a disorder in a classification benchmark_, Pattern Recognition Letters, Volume 73, 2016.
  Downloaded from https://relational.fit.cvut.cz/dataset/Bupa


More examples are included in the standalone version of =PASCAL= at https://github.com/friguzzi/pascal
The standalone version of =PASCAL= can be installed as a SWI-Prolog pack http://www.swi-prolog.org/pack/list
The other datasets include Carcinogenesis, Cora, Hepatitis, HIV, IMDB,
Mondial, UWCSE and WebKB.
They have not been included here because of their computational cost.

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