Uncertainty in Artificial Intelligence
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Multi-objective Influence Diagrams
Radu Marinescu, Abdul Razak, Nic Wilson
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.
Keywords:
Pages: 574-583
PS Link:
PDF Link: /papers/12/p574-marinescu.pdf
BibTex:
@INPROCEEDINGS{Marinescu12,
AUTHOR = "Radu Marinescu and Abdul Razak and Nic Wilson",
TITLE = "Multi-objective Influence Diagrams",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "574--583"
}


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