  The paper presents a new method for approximating Strong Stackelberg
Equilibrium in general-sum sequential games with imperfect information and
perfect recall. The proposed approach is generic as it does not rely on any
specific properties of a particular game model. The method is based on
iterative interleaving of the two following phases: (1) guided Monte Carlo Tree
Search sampling of the Follower's strategy space and (2) building the Leader's
behavior strategy tree for which the sampled Follower's strategy is an optimal
response. The above solution scheme is evaluated with respect to expected
Leader's utility and time requirements on three sets of interception games with
variable characteristics, played on graphs. A comparison with three
state-of-the-art MILP/LP-based methods shows that in vast majority of test
cases proposed simulation-based approach leads to optimal Leader's strategies,
while excelling the competitive methods in terms of better time scalability and
lower memory requirements.
