2012/10/16 by Marinescu, Radu, Razak, Abdul, Wilson, Nic
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1210.4911
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.