# Tyche: A library for probabilistic reasoning and belief modelling in Python

arXiv: https://arxiv.org/abs/2208.09838 · HTML: https://mlsanity.com/paper/2208.09838/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 21 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Neural Logic Reasoning](https://mlsanity.com/paper/2008.09514/) (2020)
- [A modal aleatoric calculus for probabilistic reasoning: extended version](https://mlsanity.com/paper/1812.11741/) (2018)
- [Aleatoric Description Logic for Probailistic Reasoning (Long Version)](https://mlsanity.com/paper/2108.13036/) (2021)

## Abstract (arXiv)

This paper presents Tyche, a Python library to facilitate probabilistic reasoning in uncertain worlds through the construction, querying, and learning of belief models. Tyche uses aleatoric description logic (ADL), which provides computational advantages in its evaluation over other description logics. Tyche belief models can be succinctly created by defining classes of individuals, the probabilistic beliefs about them (concepts), and the probabilistic relationships between them (roles). We also introduce a method of observation propagation to facilitate learning from complex ADL observations. A demonstration of Tyche to predict the author of anonymised messages, and to extract author writing tendencies from anonymised messages, is provided. Tyche has the potential to assist in the development of expert systems, knowledge extraction systems, and agents to play games with incomplete and probabilistic information.