  In zero-sum games, the optimal strategy is well-defined by the Nash
equilibrium. However, it is overly conservative when playing against suboptimal
opponents and it can not exploit their weaknesses. Limited look-ahead game
solving in imperfect-information games allows defeating human experts in
massive real-world games such as Poker, Liar's Dice, and Scotland Yard.
However, since they approximate Nash equilibrium, they tend to only win
slightly against weak opponents. We propose methods combining limited
look-ahead solving with an opponent model in order to 1) approximate a best
response in large games or 2) compute a robust response with control over the
robustness of the response. Both methods can compute the response in real time
to previously unseen strategies. We present theoretical guarantees of our
methods. We show that existing robust response methods do not work combined
with limited look-ahead solving of the shelf, and we propose a novel solution
for the issue. Our algorithm performs significantly better than multiple
baselines in smaller games and outperforms state-of-the-art methods against
SlumBot.
