ML Anthology

ILP Experiments in Detecting Traffic Problems

Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure
ECML-PKDD 1998 pp. 61-66
doi:10.1007/BFB0026673 /ecmlpkdd/1998/dzeroski1998ecml-ilp/

Abstract

Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. Preliminary results show that ILP can be used to successfully learn to detect traffic problems.

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Cite

Text

Dzeroski et al. "ILP Experiments in Detecting Traffic Problems." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026673

Markdown

[Dzeroski et al. "ILP Experiments in Detecting Traffic Problems." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/dzeroski1998ecml-ilp/) doi:10.1007/BFB0026673

BibTeX

@inproceedings{dzeroski1998ecml-ilp,
  title     = {{ILP Experiments in Detecting Traffic Problems}},
  author    = {Dzeroski, Saso and Jacobs, Nico and Molina, Martín and Moure, Carlos},
  booktitle = {European Conference on Machine Learning},
  year      = {1998},
  pages     = {61-66},
  doi       = {10.1007/BFB0026673},
  url       = {https://mlanthology.org/ecmlpkdd/1998/dzeroski1998ecml-ilp/}
}