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Efficient Algorithms and Representations for Chance-Constrained Mixed Constraint Programming

Cheng Fang
IJCAI 2017 pp. 5179-5180
doi:10.24963/IJCAI.2017/749 /ijcai/2017/fang2017ijcai-efficient/

Abstract

Resistance to adoption of autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by approaches that guarantee the probability of success. This is achieved in chance-constrained constraint programming (CC-CP) by imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel uncertainty representations to address problems prevalent in current methods.

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Cite

Text

Fang. "Efficient Algorithms and Representations for Chance-Constrained Mixed Constraint Programming." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/749

Markdown

[Fang. "Efficient Algorithms and Representations for Chance-Constrained Mixed Constraint Programming." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/fang2017ijcai-efficient/) doi:10.24963/IJCAI.2017/749

BibTeX

@inproceedings{fang2017ijcai-efficient,
  title     = {{Efficient Algorithms and Representations for Chance-Constrained Mixed Constraint Programming}},
  author    = {Fang, Cheng},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5179-5180},
  doi       = {10.24963/IJCAI.2017/749},
  url       = {https://mlanthology.org/ijcai/2017/fang2017ijcai-efficient/}
}