ML Anthology

Solving Asymmetric Decision Problems with Influence Diagrams

Runping Qi, Nevin Lianwen Zhang, David L. Poole
UAI 1994 pp. 491-497
doi:10.1016/B978-1-55860-332-5.50067-5 /uai/1994/qi1994uai-solving/

Abstract

While influence diagrams have many advantages as a representation framework for Bayesian decision problems, they have a serious drawback in handling asymmetric decision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A considerable amount of unnecessary computation may be involved when a symmetrized influence diagram is evaluated by conventional algorithms. In this paper we present an approach for avoiding such unnecessary computation in influence diagram evaluation.

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Text

Qi et al. "Solving Asymmetric Decision Problems with Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50067-5

Markdown

[Qi et al. "Solving Asymmetric Decision Problems with Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/qi1994uai-solving/) doi:10.1016/B978-1-55860-332-5.50067-5

BibTeX

@inproceedings{qi1994uai-solving,
  title     = {{Solving Asymmetric Decision Problems with Influence Diagrams}},
  author    = {Qi, Runping and Zhang, Nevin Lianwen and Poole, David L.},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1994},
  pages     = {491-497},
  doi       = {10.1016/B978-1-55860-332-5.50067-5},
  url       = {https://mlanthology.org/uai/1994/qi1994uai-solving/}
}