Using Performance Profile Trees to Improve Deliberation Control

Performance profile trees have recently been proposed as a theoretical basis for fully normative deliberation control. In this paper we conduct the first experimental study of their feasibility and accuracy in making stopping decisions for anytime algorithms on optimization problems. Using data and algorithms from two different real-world domains, we compare performance profile trees to other well-established deliberation-control techniques. We show that performance profile trees are feasible in practice and lead to significantly better deliberation control decisions. We then conduct experiments using performance profile trees where deliberationcontrol decisions are made using conditioning on multiple features of the solution to illustrate that such an approach is feasible in practice.