# Pack PASCAL

**URL:** <https://swi-prolog.discourse.group/t/pack-pascal/4866>\
**Category:** Announce\
**Created:** [January 11, 2022, 10:44am UTC](https://swi-prolog.discourse.group/t/pack-pascal/4866 "2022-01-11T10:44:45Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![friguzzi](https://yyz2.discourse-cdn.com/free1/user_avatar/swi-prolog.discourse.group/friguzzi/32/7045_2.png) [@friguzzi](https://swi-prolog.discourse.group/u/friguzzi)\
**Post date:** [January 11, 2022, 10:44am UTC](https://swi-prolog.discourse.group/t/pack-pascal/4866/1 "2022-01-11T10:44:45Z")

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pascal (probabilistic inductive constraint logic) is a new pack containing an algorithm for learning probabilistic integrity constraints. It was proposed in

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)

It contains modules for both structure and parameter learning.

You can find the manual at [http://friguzzi.github.io/pascal/](http://friguzzi.github.io/pascal/).

You can try it online at [http://cplint.eu](http://cplint.eu/).

In particular, see the example at [https://cplint.eu/e/pascal\_examples.swinb](https://cplint.eu/e/pascal_examples.swinb)

Best  
Fabrizio