---
title: "First-Order Model Counting in a Nutshell"
authors:
  - "Guy Van den Broeck"
doi: ""
url: "https://ijcai.org/proceedings/2016/607"
pdf: "https://ijcai.org/proceedings/2016/0607.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4086-4088"
track: ""
keywords:
---

# First-Order Model Counting in a Nutshell

Guy Van den Broeck

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 4086-4088.

## Abstract

First-order model counting recently emerged as a computational tool for high-level probabilistic reasoning. It is concerned with counting satisfying assignments to sentences in first-order logic and upgrades the successful propositional model counting approaches to probabilistic reasoning. We give an overview of model counting as it is applied in statistical relational learning, probabilistic programming, databases, and hybrid reasoning. A short tutorial illustrates the principles behind these solvers. Finally, we show that first-order counting is a fundamentally different problem from the propositional counting techniques that inspired it.