Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges

Michael Sioutis; Diedrich Wolter

IJCAI 2021poster22 citations

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.

Knowledge representation and reasoning: General

BibTeX

@inproceedings{ijcai2021p624,
  title     = {Qualitative Spatial and Temporal Reasoning: Current Status and Future Challenges},
  author    = {Sioutis, Michael and Wolter, Diedrich},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4594--4601},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/624},
  url       = {https://doi.org/10.24963/ijcai.2021/624},
}