---
title: "Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges"
authors:
  - "Michael Sioutis"
  - "Diedrich Wolter"
doi: "10.24963/ijcai.2021/624"
url: "https://ijcai.org/proceedings/2021/624"
pdf: "https://ijcai.org/proceedings/2021/0624.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4594-4601"
track: "Survey Track"
keywords:
  - "Knowledge representation and reasoning: General"
---

# Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges

Michael Sioutis, Diedrich Wolter

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4594-4601. DOI: [10.24963/ijcai.2021/624](https://doi.org/10.24963/ijcai.2021/624)

## Abstract

Qualitative Spatial & Temporal Reasoning (QSTR)
is a major field of study in Symbolic AI that deals
with the representation and reasoning of spatio-
temporal information in an abstract, human-like
manner. We survey the current status of QSTR
from a viewpoint of reasoning approaches, and
identify certain future challenges that we think
that, once overcome, will allow the field to meet
the demands of and adapt to real-world, dynamic,
and time-critical applications of highly active areas
such as machine learning and data mining.