---
title: "Stochastic and-or Grammars: A Unified Framework and Logic Perspective"
authors:
  - "Kewei Tu"
doi: ""
url: "https://ijcai.org/proceedings/2016/377"
pdf: "https://ijcai.org/proceedings/2016/0377.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2654-2659"
track: ""
keywords:
---

# Stochastic and-or Grammars: A Unified Framework and Logic Perspective

Kewei Tu

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2654-2659.

## Abstract

Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of language to model other types of data such as images and events. In this paper we propose a representation framework of stochastic AOGs that is agnostic to the type of the data being modeled and thus unifies various domain-specific AOGs. Many existing grammar formalisms and probabilistic models in natural language processing, computer vision, and machine learning can be seen as special cases of this framework. We also propose a domain-independent inference algorithm of stochastic context-free AOGs and show its tractability under a reasonable assumption. Furthermore, we provide two interpretations of stochastic context-free AOGs as a subset of probabilistic logic, which connects stochastic AOGs to the field of statistical relational learning and clarifies their relation with a few existing statistical relational models.