Codebase for plCoP, a Prolog Technology Reinforcement Learning Prover

Paper: https://arxiv.org/abs/2004.06997

Download project:

git clone --recurse-submodules git@github.com:zsoltzombori/plcop.git

This project makes use of two external repositories:

• PySwip for Python-Prolog interaction: https://github.com/yuce/pyswip
• Hashtbl for prolog hash tables: https://github.com/gergo-/hashtbl

To install pyswip:

cd pyswip
pip install -e .
cd ..

The xgb foreign interface is provided in

foreign/xgb.so

However, depending on your system configuration, you may need to recompile it. See

foreign/INSTALL1

for an example how to do it. For the recompilation, make sure that the xgboost c library is in /tmp/:

cp foreign/libxgboost.so /tmp/

If you need to obtain another version of xgboost, this link can help:

https://xgboost.readthedocs.io/en/latest/build.html#building-the-shared-library

Usage

To run Monte Carlo Tree Search on a single tptp problem:

python montecarlo.py ini/plcop0.ini --problem_file <path_to_file>

To run a few iterations of learning on a directory of problems, see e.g.:

bash baseline.sh