Inference in Statistical Relational AI

A tutorial on Statistical Relational AI at the ICCS 2019

24th International Conference on Conceptual Structures, July 1st-4th 2019, University Marburg

In recent years, a need for efficient inference algorithms on compact representations of large relational databases became apparent, e.g., in natural language understanding, machine learning, or decision making. This need has lead to advances in probabilistic relational modelling for artificial intelligence (also called statistical relational AI). Probabilistic relational models combine the fields of reasoning under uncertainty and modelling incorporating relations and objects in the vain of first-order logic.

Presenters

• Tanya Braun
• Marcel Gehrke

Contents

1. Introduction
2. Probabilistic (relational) modeling
   • Models 
   • Semantics
   • Inference problems
3. Exact inference
   • Scalable static inference
     • Variable elimination (VE), lifted VE
     • Junction tree algorithm (JT), lifted JT
   • Scalable temporal inference
     • Interface algorithm (IA), lifted dynamic JT
4. Summary

Download Presentations

1. Introduction
2. Probabilistic relational modeling
3. Exact (lifted) inference
   • Static inference
   • Dynamic inference
4. Wrap-up