# 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.