Multi-objective Influence Diagrams
Artificial Intelligence
2012-10-19 v1
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
@article{arxiv.1210.4911,
title = {Multi-objective Influence Diagrams},
author = {Radu Marinescu and Abdul Razak and Nic Wilson},
journal= {arXiv preprint arXiv:1210.4911},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)