TY - CHAP
T1 - Multi-objective influence diagrams
AU - Marinescu, Radu
AU - Razak, Abdul
AU - Wilson, Nic
PY - 2012
Y1 - 2012
N2 - 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 coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user tradeoffs, which also greatly improves the efficiency.
AB - 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 coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user tradeoffs, which also greatly improves the efficiency.
UR - https://www.scopus.com/pages/publications/84885750511
M3 - Chapter
AN - SCOPUS:84885750511
SN - 9780974903989
T3 - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
SP - 574
EP - 583
BT - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
T2 - 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
Y2 - 15 August 2012 through 17 August 2012
ER -