PASCAL

Pascal (probabilistic inductive constraint logic) is 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

It contains modules for both structure and parameter learning.

You can find the manual at http://friguzzi.github.io/pascal/.

You can try it online at http://cplint.eu.

Installation

This is an SWI-Prolog pack.

It can be installed with pack_install/1

$ swipl
?- pack_install(pascal).

You can upgrade the pack with

$ swipl
?- pack_upgrade(pascal).

Requirements

It requires the pack lbfgs https://github.com/friguzzi/lbfgs

It is installed automatically when installing pack pascal or can be installed manually as

$ swipl
?- pack_install(lbfgs).

Example of use

$ cd <pack>/pascal/prolog/examples
$ swipl
?- [bongardkeys].
?- induce_pascal([train]),T).

Testing the installation

$ swipl
?- [library(test_pascal)].
?- test_pascal.

Support

Use the Google group https://groups.google.com/forum/#!forum/cplint.