Algorithms for Multi-criteria optimization in Possibilistic Decision Trees

Abstract

This paper raises the question of solving multi-criteria sequential decision problems under uncertainty. It proposes to extend to possibilistic decision trees the decision rules presented in [1] for non sequential problems. It present a series of algorithms for this new framework: Dynamic Programming can be used and provide an optimal strategy for rules that satisfy the property of monotonicity. There is no guarantee of optimality for those that do not - hence the definition of dedicated algorithms. This paper concludes by an empirical comparison of the algorithms.