Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges
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.
Cite
Text
Sioutis and Wolter. "Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/624Markdown
[Sioutis and Wolter. "Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/sioutis2021ijcai-qualitative/) doi:10.24963/IJCAI.2021/624BibTeX
@inproceedings{sioutis2021ijcai-qualitative,
title = {{Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges}},
author = {Sioutis, Michael and Wolter, Diedrich},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4594-4601},
doi = {10.24963/IJCAI.2021/624},
url = {https://mlanthology.org/ijcai/2021/sioutis2021ijcai-qualitative/}
}