
PyReason
PyReason is a powerful Python-based temporal first-order logic explainable AI system supporting multi-step inference, uncertainty, open-world reasoning, and graph-based syntax.
PyReason is a logic based framework designed for neuro symbolic AI.
NEW PyReason Gym is now available: https://github.com/lab-v2/pyreason-gym
Check out our talk at AAAI-MAKE: https://youtu.be/G4-jcb2ktKg
• Supports generalized annotated logic with temporal, graphical and uncertainty extensions, capturing a wide variety of fuzzy, real-valued, interval, and temporal logics
• Modern Python-based system supporting reasoning on graph-based data structures (e.g., exported from Neo4j, GraphML, etc.)
• Rule-based reasoning in a manner that support uncertainty, open-world reasoning, non-ground rules, quantification, etc., agnostic to selection of t-norm, etc.
• Fast, highly optimized, correct fixpoint-based deduction allows for explainable AI reasoning, scales to graphs with over 30 million edges
Read the PyReason paper (w. supplement):
https://arxiv.org/pdf/2302.13482.pdf
Introductory blog post:
https://medium.com/towards-nesy/pyreason-software-for-open-world-temporal-logic-d67de751830e
Open source Python library is available at: pypi.org/project/pyreason
PyReason codebase can be found at:
github.com/lab-v2/pyreason
Install with pip:
pip install pyreason
Videos:
Introduction: https://youtu.be/E1PSl3KQCmo
Technical talk: https://youtu.be/G4-jcb2ktKg
Slides:
Introduction to PyReason v 1.1
Bibtex citation:
@inproceedings{aditya_pyreason_2023,
title = {{PyReason}: Software for Open World Temporal Logic},
booktitle = {{AAAI} Spring Symposium},
author = {Aditya, Dyuman and Mukherji, Kaustuv and Balasubramanian, Srikar and Chaudhary, Abhiraj and Shakarian, Paulo},
year = {2023} }