  Multistage robust optimization problems can be interpreted as two-person
zero-sum games between two players. We exploit this game-like nature and
utilize a game tree search in order to solve quantified integer programs
(QIPs). In this algorithmic environment relaxations are repeatedly called to
asses the quality of a branching variable and for the generation of bounds. A
useful relaxation, however, must be well balanced with regard to its quality
and its computing time. We present two relaxations that incorporate scenarios
from the uncertainty set, whereby the considered set of scenarios is
continuously adapted according to the latest information gathered during the
search process. Using selection, assignment, and runway scheduling problems as
a testbed, we show the impact of our findings.
