Multi-Objective Influence Diagrams
Abstract
We describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can still be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on e-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user tradeoffs, which also greatly improves the efficiency.
Cite
Text
Marinescu et al. "Multi-Objective Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Marinescu et al. "Multi-Objective Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/marinescu2012uai-multi/)BibTeX
@inproceedings{marinescu2012uai-multi,
title = {{Multi-Objective Influence Diagrams}},
author = {Marinescu, Radu and Razak, Abdul and Wilson, Nic},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {574-583},
url = {https://mlanthology.org/uai/2012/marinescu2012uai-multi/}
}